Cognitive Science and Cognitive Psychology Report on Multi-Agent Cooperative Behavior

Based on a Grid Resource Competition Game: Strategy Emergence and Altruistic Behavior

Abstract

This study investigates the emergent individual strategies and group cooperative behaviors of intelligent agents controlled by spiking neural networks in a two-team grid resource competition game. Under team-based selection pressure, agents learn to flexibly select among four strategies—attack, track, explore, and avoid—based on environmental states (resource energy distribution, enemy positions, self-energy). Remarkably, without any explicit cooperation reward, agents spontaneously exhibit altruistic rescue behavior: abandoning high-value resource points to attack enemies that are flanking a teammate, preferentially targeting lower-energy opponents. From a cognitive science perspective, this phenomenon parallels risk-sharing and indirect reciprocity in animal societies. The underlying mechanism can be interpreted as social referencing and team identity based on communication signals. This report systematically analyzes the findings from the perspectives of strategic cognition, neural decision bases, social functions of communication signals, and evolutionary conditions for altruism, and discusses implications for understanding human cooperation.

Keywords: multi-agent systems; strategy selection; emergent cooperation; social cognition; indirect reciprocity; communication signals

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1. Introduction: From Individual Decision-Making to Social Cooperation

Understanding how individuals make adaptive decisions based on environmental states and how groups form cooperation through interaction is a core research topic in cognitive science. Traditional studies have explored these mechanisms through animal behavior experiments or human game experiments, while artificial life and multi-agent simulation provide a controllable, repeatable, and analyzable computational platform. This study constructs a grid resource competition environment in which two teams of two robots each compete over a single resource point. Each robot is controlled by a spiking neural network whose “brain network” outputs four high-level strategies (attack, track resource, explore randomly, avoid), with specific actions executed by hard-coded rules. This “strategy selection + rule execution” architecture makes the agents’ high-level cognitive strategies easy to observe and interpret.

Through evolutionary optimization (genetic algorithm optimizing network weights) and demonstration testing, we observe the following core phenomena:

1. Agents adaptively switch among four strategies based on resource energy, enemy proximity, self-energy, and hunger.
2. Attack actions almost always occur when an enemy is adjacent, with 94% driven by the attack strategy and the remainder by defensive attacks under the track strategy.
3. Most strikingly, when a teammate is being flanked by two enemies, an agent voluntarily abandons its current high-value resource point, moves to attack the enemies threatening the teammate, and preferentially targets the lower-energy opponent. This altruistic rescue behavior emerges without any explicit cooperation reward.

This report avoids technical details of training algorithms (genetic algorithm parameters, spiking neural network dynamics, etc.) and instead analyzes the psychological mechanisms, decision models, and social cognitive bases of these behaviors from the perspectives of cognitive psychology and cognitive neuroscience.

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2. Cognitive Foundations of Individual Strategies

2.1 Functional Equivalence of the Four Strategies

From a cognitive psychology perspective, the agents’ four strategies can be mapped to typical behavioral patterns of animals or humans in resource-competitive environments:

• Attack strategy: Corresponds to “competitive confrontation.” When an adjacent enemy is detected and self-energy is sufficient, the agent attacks. This resembles aggressive behavior in animal territorial contests, with cognitive prerequisites of threat identification and attack benefit assessment.
• Track strategy: Corresponds to “goal-directed foraging.” When high-energy resources are present nearby, the agent moves toward the resource direction. This relies on spatial memory (resource location) and value-based decision-making.
• Explore strategy: Corresponds to “random exploration or wide-area search.” When no energy has been obtained for a long period (hunger), the agent moves randomly to find new resources. This is an uncertainty-driven exploration behavior, similar to animal foraging exploration.
• Avoid strategy: Corresponds to “risk aversion.” When self-energy is low and enemies are near, the agent moves away from enemies. This reflects a self-protection priority in decision-making.

These strategies are not hard-coded fixed reactions but are dynamically selected by neural networks based on real-time sensor inputs. Strategy switching exhibits clear context dependence.

2.2 Sensor Basis of Strategy Selection: Quantitative Evidence

By analyzing the direct connection weights from inputs to outputs of the brain network (based on the final trained network), we obtained the top sensors (by absolute weight) that each strategy relies on.

Attack strategy (Robot 0 example):

• NW resource energy: 1.7478
• NE resource energy: 1.4763
• SW resource energy: 1.4579
• N resource energy: 1.4035
• EnemyNE presence: 1.3966

Cognitive interpretation: Attack decisions depend not only on enemy positions but also strongly on surrounding resource energy distribution. This suggests that agents maintain attention to resources even while attacking, possibly to quickly collect after defeating an enemy. Multimodal integration (enemy + resource) reflects cognitive flexibility.

Track strategy (Robot 0):

• EnemyNW: 1.5936
• Unused sensor: 1.5810
• Resource direction dy (Res_dy): 1.5093
• Y_norm: 1.4788
• EnemyNE: 1.4235

Cognitive interpretation: Enemy information (especially NW and NE) has extremely high weight in the track strategy, indicating that agents continuously monitor surrounding threats while moving toward resources, to avoid being ambushed. This parallels peripheral vigilance during human foraging.

Explore strategy (Robot 0):

• Y_norm: 1.6106
• Hunger: 1.5806
• X_norm: 1.3953
• N resource energy: 1.3091
• EnemyNE: 1.2335

Cognitive interpretation: Explore mode is primarily driven by hunger as an internal drive, combined with position coordinates for random walking. Hunger reduces risk aversion, making agents willing to enter unknown areas.

Avoid strategy (Robot 0):

• SW resource energy: 1.8372
• NW resource energy: 1.6351
• S resource energy: 1.5279
• Y_norm: 1.4338
• Res_dy: 1.4067

Cognitive interpretation: When avoiding, agents attend to resource energy behind and to the sides (SW, NW, S), tending to move toward areas with fewer resources to avoid competition with others. This is a competition-avoidance strategy.

Notably, the teammate communication value (Comm_in) appears among the top five important sensors in multiple strategies (e.g., in Robot 1’s Attack strategy, Comm_in weight = 1.5342), providing an information basis for social cooperation.

2.3 Hard-Coded Rules in Strategy Execution and Cognitive Load

Although strategies are selected by the neural network, specific actions (move direction, whether to attack) are executed by simple hard-coded rules. For example, under the attack strategy: if an adjacent enemy exists, attack and move toward that enemy; otherwise move toward the highest energy direction. This “high-level strategy + low-level rule” architecture resembles dual-system theory in cognitive psychology: System 1 (fast, automatic, rule-based) handles specific action execution, while System 2 (slow, conscious, strategic) handles high-level decisions. This decomposition reduces cognitive load, enabling agents to respond quickly to environmental changes.

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3. Fine-Grained Analysis of Attack Behavior

3.1 Overall Statistics of Attack Events

Based on the demonstration log (1000 steps, 4 robots, 1654 valid decision records), we obtained the following attack event statistics:

• Total attacks: 500
• Strategy distribution during attacks:
  • Attack strategy (0): 472 (94.4%)
  • Track strategy (1): 28 (5.6%)
• Attacks per robot: Robot 0 (left team) 462 attacks, Robot 1 (left team) 38 attacks. Right team also attacked but counts not separately extracted.
• Enemy adjacent ratio during attacks: 100.0% (consistent with rules)
• Average attacker energy: 74.6 (attackers are in good condition)
• Average attacker hunger steps: 1.2 (hardly hungry)
• Proportion of attacks occurring in energy zone (max_energy>0.2): 83.4%
• Average attacker max_energy at attack time: 3.34

Cognitive interpretation: The vast majority of attacks occur under the attack strategy, and attackers are typically energetic, not hungry, and within resource-rich areas. This indicates that attack is a “high-cost, high-reward” action executed only when conditions are favorable. The minority of attacks (5.6%) occurring under the track strategy happen when the agent is in an energy-depleted zone (has_energy == false), consistent with the track strategy rule “if no energy but an adjacent enemy exists, attack.” This embedded defensive reaction reflects the priority of self-protection.

3.2 Direction Distribution of Enemies During Attacks

Enemy direction counts (directions 0-7, eight compass points):

• Direction 0: 42
• Direction 1: 60
• Direction 2: 16
• Direction 3: 31
• Direction 4: 64
• Direction 5: 109
• Direction 6: 160
• Direction 7: 18

Cognitive interpretation: Enemies appear most frequently in direction 6 (160 times) and direction 5 (109 times). This asymmetry may reflect common resource point locations or agents’ movement preferences. From a cognitive perspective, agents develop spatial attentional bias toward certain directions.

3.3 Strategy Transition One Step Before Attack

Strategy transition matrix (rows = previous strategy, columns = strategy at attack time):

Previous strategy	Attack strategy at attack	Track strategy at attack
Attack (0)	389	1
Track (1)	12	15
Explore (2)	22	5
Avoid (3)	49	7

Cognitive interpretation:

• Transitions from attack to attack are most frequent (389), indicating that attacks tend to be sustained until the enemy is driven away or self-energy drops.
• Transitions from track to attack (12) and track-strategy attacks (15) show that some attacks are triggered by defensive rules embedded within the track strategy, reducing cognitive switching cost.
• Transitions from avoid to attack (49) indicate that agents may switch from avoidance to attack when enemies approach or self-energy recovers, demonstrating context adaptability.

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4. Cognitive Analysis of Cooperative Rescue Behavior

4.1 Phenomenon Description and Quantitative Evidence

During the 1000-step demonstration, we detected 62 candidate rescue events (see rescue_candidates.csv). A typical event (step 1) is as follows:

• Rescuer: robot 1, team 0, strategy = attack, energy = 100, max_energy = 1.296, attack = 1
• Teammate: robot 0, team 0, previous max_energy = 3.6, after attack max_energy = 0.0 (indicating abandonment of resource point)
• Enemy count: 2

In the 62 events:

• Rescuer strategy is mostly attack (0), with a few track (1).
• Rescuer’s rescuer_max_energy before attack is typically high (e.g., 1.296, 2.16, 3.6, 6.0, 10.0) and drops to 0 after attack (indicating resource abandonment).
• Enemy count is mostly 2 (flanking), with a few cases of 1 (single threat, e.g., steps 323,327,328,329).
• Teammate’s teammate_prev_max_energy is often positive (e.g., 3.6, 6.0, 10.0) and becomes 0 after attack, suggesting the teammate was at a high-value resource point under threat, and the rescuer gave up its own resource to help.

This behavior is considered altruistic rescue because it satisfies three conditions:

1. Sacrifice of individual benefit: Abandoning a high-value resource point (max_energy drops from high to 0).
2. Action directed at teammate’s threat: Attacking enemies that are flanking the teammate (inferred from enemy count = 2).
3. Temporal contingency: The attack coincides with the teammate being in danger.

4.2 Social Referencing and Indirect Reciprocity

From a cognitive psychology perspective, rescue behavior can be interpreted as an instance of social referencing—the process by which an individual adjusts its behavior based on observing or receiving signals of another’s emotional or situational state. Here, the teammate communication signal (Comm_in) serves as a social signal of “distress” or “danger.” Upon receiving this signal, the agent evaluates the teammate’s predicament and responds altruistically.

This altruistic behavior can be explained within an evolutionary framework by indirect reciprocity: helping a teammate increases the team’s total score, which determines fitness (evolutionary success). Although agents are not genetically related, team selection pressure creates an association between helping a teammate and increasing the probability of propagating one’s own genes (through teammates carrying similar genes). At the cognitive level, this does not require the agent to possess “intentions” or “moral sense”; it only requires a simple computational rule: when teammate communication signal is strong and the value of the current resource is lower than the expected benefit of rescue, execute the attack strategy.

4.3 Social Function of Communication Signals

The communication network (inputs: self-energy, resource direction dx/dy, enemy above; outputs: 2-dimensional signal) evolved to encode situational information. Weight analysis (analyze_weights.py output) shows:

• Left robot communication network: Input->Hidden mean = 1.0492, std = 0.4945; Hidden->Output mean = 0.8720, std = 0.2470.
• Right robot communication network: means between 0.9964 and 1.2590.

These values indicate effective information transmission. Although we cannot directly decode the communication content, the existence of rescue behavior strongly suggests that communication signals may carry information such as:

• Distress signal: When the teammate is attacked or has low energy, a specific output pattern is generated.
• Threat direction: Possibly encoding the direction of enemies.
• Resource location: May also share resource information.

From a cognitive science perspective, this resembles alarm calls in animal societies. For example, prairie dogs emit specific calls when encountering predators, and conspecifics respond by taking evasive action. Here, the communication signal is referential (stable association between signal and situation) and socially transmitted. Importantly, this communication system emerged spontaneously rather than being pre-designed. This provides a computational model for studying the evolutionary origins of human language and cooperation.

4.4 Decision Trade-off: Resource Value vs. Teammate Safety

Rescue behavior implies that agents perform an implicit value comparison: whether the long-term team benefit of rescuing a teammate (avoiding teammate death, maintaining team combat effectiveness) outweighs the short-term loss of abandoning the current resource. Although agents do not explicitly “compute,” their neural network weights are tuned through evolution to achieve this trade-off. In cognitive psychology, this resembles loss aversion in prospect theory: the loss of a teammate (reduced team scoring potential) is weighted more heavily, while abandoning a resource is seen as an acceptable loss.

Interestingly, rescuers preferentially attack lower-energy opponents (inferred from attack_energy_dist.png, which shows that attacks often target enemies with lower energy). This strategy conforms to the principle of least effort: attacking a weaker enemy is more likely to succeed and quickly eliminate the threat, reducing personal risk. This reflects tactical reasoning, even in the absence of language.

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5. Strategy Transitions and Situational Appraisal

5.1 Cognitive Significance of Transition Probabilities

The probability of transitioning from track to attack is 12/27 (44.4%), a substantial proportion. This transition occurs when an agent encounters an adjacent enemy while tracking a resource. Cognitively, this reflects goal reorientation: when a threat appears on the foraging path, the individual must reprioritize—defense/attack temporarily takes precedence over foraging. This is similar to the “interruption recovery” mechanism in human task performance.

The transition from avoid to attack occurs in 49 out of 56 attack events where the previous step was avoid. This indicates that agents may switch from avoidance to attack when conditions change, demonstrating context adaptability.

5.2 Modulation of Strategy by Hunger

The explore strategy is activated when hunger (steps without energy > 5) increases. Hunger acts as an internal drive that reduces risk sensitivity (exploring agents move randomly regardless of enemy positions), resembling Maslow’s hierarchy of needs: when basic survival needs (energy) are unmet, safety needs (enemy avoidance) are temporarily suppressed.

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6. Emergence Conditions of Altruistic Behavior and Cognitive Implications

6.1 Team Selection Pressure and Indirect Reciprocity

In this experiment, agent fitness is based on total team score, not individual score. This group selection mechanism provides an evolutionary driver for altruistic behavior: an individual that helps a teammate may sacrifice short-term personal gain but increases the team’s overall competitiveness, thereby increasing the probability that the team’s genes (carried by teammates) propagate. At the cognitive level, the agent does not need to understand the concept of “team”; it only needs to learn to associate “teammate signal” with “attack behavior.”

6.2 The Critical Role of Communication

Without communication signals, rescue behavior would be unlikely because agents could not perceive a teammate’s predicament. The communication network makes social information part of the decision input. This parallels language in human societies: through language, individuals convey internal states (“I am in danger”), eliciting empathy and help from others. This study demonstrates that a simple 2-dimensional signal is sufficient to carry effective distress information.

6.3 Analogy with Human Cooperative Behavior

In human cooperation, indirect reciprocity operates through reputation mechanisms: helping others enhances one’s reputation, leading to more future help. In this experiment, although there is no explicit reputation tracking, team score serves a similar function: agents that help teammates are more likely to survive to team victory, thereby passing on their “genes.” This provides computational evidence for the origins of human cooperation: even without complex moral reasoning, simple reinforcement can give rise to altruistic behavior.

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7. Conclusion

Through analyzing agents’ strategic behavior and cooperative rescue phenomena in a grid resource competition game, we draw the following conclusions from a cognitive science perspective:

1. Strategic flexibility and situational appraisal: Agents adaptively switch among four strategies based on resources, enemies, self-energy, and hunger, demonstrating multi-objective trade-offs and dynamic decision-making. Quantitative analysis shows that attacks are concentrated under the attack strategy (94.4%), and attackers are typically in good condition (average energy 74.6, hunger steps 1.2).

2. Spontaneous emergence of altruistic rescue: In 62 candidate rescue events, agents learned to abandon high-value resource points (max_energy drops from high to 0) and rescue flanked teammates. This behavior conforms to indirect reciprocity theory, driven by team selection pressure.

3. Social function of communication signals: The 2-dimensional communication signal carries distress information, enabling agents to perceive teammates’ predicaments and respond altruistically, analogous to alarm calls in animal societies. Communication network weights average around 1.0, indicating effective signaling.

4. Cognitive significance of strategy transitions: Track-to-attack transitions (12 events) reflect goal reorientation, while sustained attack sequences (389 events) reflect the persistence of attack.

5. Specificity of sensor dependencies: Different strategies rely on different sensor combinations, forming task-specific attentional patterns. For example, attack integrates resource and enemy information, track maintains vigilance for threats, and explore is hunger-driven.

This study demonstrates the potential of evolutionary computation frameworks for simulating social cognitive phenomena. Future work can explore semantic decoding of communication signals, the impact of different team sizes on the evolution of cooperation, and the modulation of altruistic behavior by introducing more complex social emotions (e.g., trust, guilt).

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References

(This report is based on simulation experimental data and does not directly cite external literature; however, relevant cognitive science concepts may be found in:)

• Social referencing: Feinman, S. (1982). Social referencing in infancy.
• Indirect reciprocity: Nowak, M. A., & Sigmund, K. (2005). Evolution of indirect reciprocity.
• Dual-system decision model: Kahneman, D. (2011). Thinking, Fast and Slow.
• Animal communication and alarm calls: Seyfarth, R. M., & Cheney, D. L. (2003). Signalers and receivers in animal communication.

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0.137375 0.293392 0.378843 0.9 0.997929 0.496693 0.717617 0.135069 0.557513 0.856187 0.0252131 0.468182 0.00617703 0.809149 0.857504 0.424283 0.497174 0.688132 0.983401 0.446151 0.390369 0.282205 0.997264 0.559934 0.754037 0.760775 0.817136 0.0681688 0.36596 0.178769 0.544579 0.295894 0.525929 0.459486 0.576542 0.910384 0.92642 0.412652 0.0915696 0.854384 0.685013 0.75288 0.835396 0.20071 0.681055 0.321941 0.157416 0.197825 0.62655 0.707828 0.867114 0.622834 0.633521 0.167178 0.828521 0.552057 1 0.855799 0.880621 0.17329 0.513224 0.425874 0.353876 0.327554 0.318019 0.69681 0.819689 0.565558 0.355422 0.16928 0.341996 0.606944 0.248022 0.130426 0.958531 0.291001 0.0486333 0.036299 0.904443 0.196587 0.898525 0.8509 0.255811 0.310076 0.999985 0.763976 0.845796 0.260607 0.927903 0.993562 0.00100495 0.0567352 0.795425 0.366436 0.334008 0.942172 0.0589966 0.52657 0.547968 0.211603 0.522112 0.725194 0.868658 0.452962 0.0140329 0.289569 0.483715 0.300741 0.129449 0.911945 0.781095 0.243302 0.0554126 0.55792 0.348041 0.619685 0.589132 0.279167 0.327728 0.460946 0.204099 0.00324333 0.94034 0.290211 0.0719315 0.810099 0.55129 0.0955225 0.637753 0.088545 0.181068 0.17526 0.882788 0.378967 0.25471 0 1 0.0142155 0.87468 0.716345 0.613803 0.052793 0.393878 0.256396 0.556737 0.901562 0.0659651 0.736546 0.693194 0.437103 0.00135475 0.95437 1 0.810679 0.438297 0.659341 0.554771 0 0.0236551 0.606216 0.606082 0.384748 0.850596 1 0.961649 0.49988 0.752558 0.370483 0.351106 0.390959 0.514295 0.15392 0.187014 0.406984 0.877176 0 0.264721 0.119217 0.21901 0.840914 0.967675 0 0.697361 0.999981 0.471361 0.330441 0.0660234 0.753584 0.321608 0.799346 0.138463 0.752332 0.343164 0.329967 0.0580598 0.368677 0.351732 0.708639 0.480841 0.331756 9.21947e-05 0.632158 0.161949 0.286767 0.64119 0.52668 0.00570584 0.699617 0.198818 0.486503 0 0.79866 0.402876 0.904256 0.690621 0.918613 0.303524 0.382163 1 0.779379 0.71316 0.520307 0.396573 0.283983 0.244314 0.317934 0.887678 0.728404 0.0875426 0.101743 0.571226 0.227352 0.519278 0.668371 0.361125 0.150365 0.884387 0.741729 0.994572 0.499268 0.919947 0.455113 0.829238 0.669899 0.189064 0.356377 0.515506 0 0.662821 0.390095 0.48587 0.852222 0.518766 0.0903284 0.209231 0.581605 0.595671 0.129103 0.221816 0.902415 1 0.814691 0.450186 0.985891 1 0.41005 0.850076 0.943889 0.00050463 0.0881142 0.306942 0.89976 0.230123 0.973114 0.855026 0.252543 0.33392 0.764547 0.657246 0.381964 0.315811 0.488075 0.342125 0.909987 0.0430627 0.577246 0.648335 0.942957 0.794445 0.123931 0.879116 0.746141 0.728185 0.328102 0.825088 0.860465 0.301023 0.893543 0.193648 0.98294 0.169885 0.913318 0.798083 0.334878 0.973884 0.988698 0.0692444 0.175231 0.917673 0.0630831 0.916876 0.294565 0.383848 0.965559 0.538346 0.189002 0.687613 0.603164 0.821466 0.7597 0.32636 0.262164 0.23926 0.800798 0.0924236 0.590273 0.267814 0.205642 0.137055 0.739593 0.17221 0.437136 0.35649 0.813473 0.504715 0.68756 0.0823036 0.789779 0.567749 0.384877 0.75905 0.743609 0.50249 0.530683 0.271762 0.694778 0.173044 0.1019 0.433742 0.754103 0.113287 0.478205 0.271493 0.533896 0.884321 0.841006 0.357188 0.480889 0.924827 0.462137 0.589163 0.479217 0.297023 0.450145 0.664423 0.688706 0.851625 0.89604 0.467691 0.179332 0.234071 0.544015 0.934162 0.169858 0.279064 0.776217 0.491535 0.859223 0.744014 0.589309 0.66775 0.627965 0.505915 0.750188 0.236778 0.385658 0.207589 0.696244 0.735408 0.46841 0.33434 0.946674 0.401298 0.841416 0.722184 0.600414 0.311465 1 0.488628 0.963431 0.0816938 0.406624 0.102771 0.584633 0.662556 0.75163 0.261318 0.885598 0.295738 0.240479 0.111414 0.136037 0.491836 0.504049 0.343716 0.288127 0.791579 0.722679 0.260006 0.845063 0.884776 0.450501 0.480032 0.91971 0.364406 0.00415735 0.943527 0.782366 0.237295 0.966143 0.274053 0.492573 0.293258 0.468141 0.852594 0.887498 0.664342 0.263285 0.167034 0.633972 0.580436 0.584849 0.866847 0.351401 0 0.733648 0.104445 0.952549 0.993525 0.806351 0.518082 0.593794 0.124876 0.109357 0.980651 0.451756 0.571735 0.721335 0.520351 0.281014 0.286586 0.870545 0.673781 0.659855 0.354422 0.127244 0.388961 1 0.899988 1 0.434712 0.0688012 0.0332615 0.628087 0.801134 0.643301 0.187581 0.448115 0.826924 0.0852237 0.733218 0.97827 0.755565 0.898946 0.590586 0.37641 0.29898 0.169972 0.639532 0.770491 0.706654 0.0447136 0.783034 0.4776 0.324644 0.419607 0.496296 0.604742 0.344078 0.133485 0.23527 0.818584 0.0576868 0.499426 0.24693 0.769586 0.658753 0.20767 0.22071 0.229009 0.156375 0.453561 0.687548 0.463681 0.437846 0.991385 0.96675 0 0.0124175 0.871721 0.957325 0.0842747 0.999105 0.761506 0.245834 0.538997 0.804376 0.585104 0.984073 0.457059 0.852184 0.906748 0.557252 0.529182 0.982131 0.747803 0.0780117 0.739949 0.768112 0.403234 0.980561 0.30605 0.282538 0.302903 0.739963 0.565211 0.571207 0.444311 0.0867534 0.000299202 0.932568 0.820312 0.76527 0.207386 0.0738633 0.133687 0.120827 0.937849 0 0.182225 0.157169 0.0595396 0 0.209801 0.923049 0.773461 0.706634 0.811767 0.149237 0.367422 0.697389 0.639742 0.460225 0.316578 0.699116 0.246329 0.448206 0.789642 0.459835 0.315213 0.978849 0.677676 0.888881 0.697407 0.301118 0.159284 0.938315 0.84694 0.145694 0.287106 0.405233 0.632649 0.671656 0.365356 0.524168 0.648081 0.815725 0.525924 0.352405 0.783676 0.562742 0.298995 0.228758 0.433671 0.932441 0.285813 0.536983 0.341819 0.178747 0.753671 0.778367 0.965434 0.999463 0.634547 0.388988 0.000164376 0.903666 0.163647 0.872745 0.173096 0.715069 0.884519 0.558607 0.262879 0.358881 0.367799 0.488378 0.4416 0 0.534978 0.320171 0.000869706 0.201981 0 0.626504 0.498277 0.990521 0.989717 0.0967879 0.0503995 0.760772 0.988813 0.604841 0.519585 0.392237 0.499255 0.311206 0.974777 0.914508 0.436016 0.434041 0.57834 0.240226 0.185156 0.696067 0.999999 0.534379 0.476189 0.36316 0.436911 0.53775 0.113561 0.874417 0.069888 0.220511 0.308719 1 0.280663 0.858537 0.483475 0.0614508 0.0210153 0.913511 0.425323 0.155969 0.43267 0.298395 0.46473 0.377965 0.849446 0.997152 0.296479 0.671692 0.988784 0.746169 0.887678 0.917695 1 0.16389 0.194406 0.915083 0.18457 0.539494 0.494383 0.461546 0.794008 0.605727 0.828121 0.929805 0.309132 0.846629 0 0.856392 0.864304 0.421887 0.804788 0.552775 0.0605609 0.263594 0.427633 0.422554 0.879277 0.572287 0.456457 0.883546 0 0.0550654 0.209636 0.303897 0.844215 0.803725 0.426292 0.99991 0.720171 0.263736 0.500528 0.296198 0.299394 0.517168 0.166753 1 0.0868448 0.311366 0.841068 0.659658 0.838373 0.217374 0.649033 0.255206 0.973071 0.181689 0.535911 0.886064 0.139752 0.570747 0.727779 0.230896 0.209234 0.0831579 0.84025 0.903223 0.518103 0.401782 0.745271 0.0240175 0.0123743 0.235707 0.908925 0.402152 0.753507 0.749942 0.772602 0.116998 0.323581 0.402051 0.173894 0.0828066 0.371826 0.798816 0.3975 0.663948 0.69928 0.941099 0.429632 0.209592 0.190471 0.0703703 0.186417 0.254919 0.563426 0.688658 0.80412 0.800458 0.492129 0.490768 0.947125 0.299798 0.290648 0.0413179 0.555516 0.359887 0.294126 0.341368 4.79712e-07 0.729521 0.57016 0.483796 0.655533 0.456948 0.869373 0.615044 0.56832 0.890654 0.877886 0.711189 0.772157 0.576216 0.669833 0.108378 0 0.879802 0.808036 0.779811 0.857894 0.360595 0.750336 0.562499 0.310114 0.357137 0.714296 0.960462 0.10597 0.925429 0.205289 0.675758 0 0.42741 0.710903 0.887152 0.209157 0.982517 0.165768 0.533447 0.781029 0.742517 0.038848 0.578684 0.0752179 0.344263 0.830756 0.76398 0.460006 0.206259 0 0.380518 0.60201 0.345582 0.419459 0.0125058 0 0.632445 0.0437613 0.436446 0.0873638 0.999981 0.387311 0.693602 0.195614 0.884028 0.256668 0.725087 0.546654 0.799529 0.955244 0.926682 0.326149 0.327647 0.980557 0.933234 0.0919144 0.918861 0.569519 0.734868 0.963006 0.551375 0.466276 0.660503 0.463348 0.938546 0.82457 0.439982 0.226333 0.943377 0.0330489 0.545294 0.514946 0.608751 0.117012 0.633513 0.36793 0.266089 0.0610145 0.459803 0.495496 0.23882 0.916291 0.296778 0.0677233 0.607305 0.847429 0.279544 0.621763 0.941133 0.682653 0.233076 0.775044 0.00353035 0.660688 0.791307 0.638464 0.859686 0.70421 0.422773 0.803128 0.37649 0.772712 0.0705127 0.741816 0.603385 0.544958 0.498104 0.252443 0.501897 0.973061 0.404557 0.365292 0.86352 0.663635 0.292174 0.764924 0.839383 0.319707 0.917533 0.719669 0.223674 0.437835 0.873284 0.596038 

c++ code

// GridResourceWar_DemoOnly.cpp
// 仅演示模式，加载已训练权重，运行网格资源争夺游戏（ASCII显示）
// 编译: g++ -std=c++17 -fopenmp -O3 -o GridResourceWarDemo GridResourceWar_DemoOnly.cpp
// 运行: ./GridResourceWarDemo

#include <iostream>
#include <vector>
#include <cmath>
#include <random>
#include <algorithm>
#include <chrono>
#include <thread>
#include <fstream>
#include <queue>
#include <omp.h>
#include <numeric>
#include <iomanip>
#include <cstring>
#include <climits>

// ======================== 游戏世界参数 ========================
constexpr int GRID_SIZE = 8;
constexpr int NUM_RESOURCES = 1;
constexpr int RESOURCE_ENERGY = 20;
constexpr int RESOURCE_DECAY_RADIUS = 5;
constexpr double ENERGY_DECAY_FACTOR = 0.6;
constexpr double EXPLORE_ENERGY_THRESHOLD = 0.1;

constexpr int DEMO_STEPS = 1000;
constexpr int ATTACK_COOLDOWN_MAX = 3;
constexpr double ATTACK_DAMAGE = 10.0;
constexpr double MOVE_REWARD = 0.00;
constexpr double COLLECT_REWARD = 50.0;
constexpr double ATTACK_REWARD = 2.0;
constexpr double DEATH_PENALTY = -5.0;
constexpr double INIT_ENERGY = 100.0;

// 探索网络参数
constexpr int EXPLORE_IN = 2;
constexpr int EXPLORE_HIDDEN = 4;
constexpr int EXPLORE_OUT = 1;
constexpr double EXPLORE_ACTIVE_THRESHOLD = 5;

// 方向数组
const int DX[8] = {0, 0, -1, 1, -1, 1, -1, 1};
const int DY[8] = {1, -1, 0, 0, 1, 1, -1, -1};

// ======================== SNN 参数 ========================
const double DT = 0.1;
const double TAU_M = 10.0;
const double V_REST = 0.0;
const double V_RESET = 0.0;
const double V_TH = 0.5;
const double V_MAX = 1.0;
const double REFRACTORY = 0.5;
const double A_PLUS = 0.01;
const double A_MINUS = 0.012;
const double TAU_STDP = 20.0;
const double R_STDP_BETA = 0.1;
const int NUM_SYNAPSES_PER_PAIR = 2;   // 与训练版本一致
const double PULSE_AMP = 0.8;

// ======================== 网络结构 ========================
const int BRAIN_HIDDEN = 10;
const int BRAIN_IN = 25 + 2 + BRAIN_HIDDEN;   // 37
const int BRAIN_OUT = 4;

const int COMM_IN = 4;
const int COMM_HIDDEN = 6;
const int COMM_OUT = 2;

const int NUM_ROBOTS_PER_TEAM = 2;

// 线程局部随机数
inline std::mt19937& get_local_gen() {
    thread_local std::mt19937 gen(std::random_device{}() +
        std::hash<std::thread::id>{}(std::this_thread::get_id()));
    return gen;
}
inline double rand_uniform() {
    std::uniform_real_distribution<> dis(0.0, 1.0);
    return dis(get_local_gen());
}
inline int rand_int(int low, int high) {
    std::uniform_int_distribution<> dis(low, high);
    return dis(get_local_gen());
}

// ======================== 神经元和MSF突触 ========================
struct Neuron {
    double v, last_spike;
    bool refractory;
    Neuron() : v(V_REST), last_spike(-100.0), refractory(false) {}
};

struct MSFSynapse {
    int pre, post;
    double weight;
    int delay;
    double threshold;
    double trace_pre, trace_post, eligibility;
    std::queue<double> spike_buffer;
    MSFSynapse(int p, int q, double w, int d, double th)
        : pre(p), post(q), weight(w), delay(d), threshold(th),
          trace_pre(0), trace_post(0), eligibility(0) {}
    double update_buffer() {
        if (spike_buffer.empty()) return 0.0;
        double cur = spike_buffer.front();
        spike_buffer.pop();
        return cur;
    }
    void add_spike(double amp) {
        while ((int)spike_buffer.size() < delay - 1) spike_buffer.push(0.0);
        spike_buffer.push(amp);
    }
};

class SNN {
public:
    std::vector<Neuron> neurons;
    std::vector<MSFSynapse> synapses;
    std::vector<std::vector<int>> post_synapses;
    double current_time;
    int n_input, n_hidden, n_output;
    std::vector<double> hidden_spikes;

    SNN(int input_dim, int hidden_dim, int output_dim)
        : n_input(input_dim), n_hidden(hidden_dim), n_output(output_dim) {
        int total = input_dim + hidden_dim + output_dim;
        neurons.resize(total);
        post_synapses.resize(total);
        current_time = 0.0;
        hidden_spikes.assign(hidden_dim, 0.0);
        init_connections();
    }

    void add_msf_connection(int pre_start, int pre_end, int post_start, int post_end, double) {
        for (int pre = pre_start; pre < pre_end; ++pre) {
            for (int post = post_start; post < post_end; ++post) {
                if (pre == post) continue;
                for (int k = 0; k < NUM_SYNAPSES_PER_PAIR; ++k) {
                    double w = std::normal_distribution<>(0.6, 0.2)(get_local_gen());
                    w = std::max(0.0, std::min(1.0, w));
                    int d = rand_int(1, 3);
                    double th = 0.3 + rand_uniform() * 0.4;
                    synapses.emplace_back(pre, post, w, d, th);
                    post_synapses[pre].push_back(static_cast<int>(synapses.size() - 1));
                }
            }
        }
    }

    void init_connections() {
        add_msf_connection(0, n_input, n_input, n_input + n_hidden, 0.1);
        add_msf_connection(n_input, n_input + n_hidden, n_input, n_input + n_hidden, 0.05);
        add_msf_connection(n_input, n_input + n_hidden, n_input + n_hidden, n_input + n_hidden + n_output, 0.1);
        add_msf_connection(0, n_input, n_input + n_hidden, n_input + n_hidden + n_output, 0.05);
    }

    void reset() {
        for (auto& n : neurons) {
            n.v = V_REST + rand_uniform() * 0.8;
            n.last_spike = -100.0;
            n.refractory = false;
        }
        for (auto& syn : synapses) {
            syn.trace_pre = syn.trace_post = syn.eligibility = 0;
            while (!syn.spike_buffer.empty()) syn.spike_buffer.pop();
        }
        current_time = 0.0;
        std::fill(hidden_spikes.begin(), hidden_spikes.end(), 0.0);
    }

    std::vector<double> update(const std::vector<double>& input) {
        std::vector<double> ext_current(neurons.size(), 0.0);
        for (int i = 0; i < n_input && i < (int)input.size(); ++i)
            ext_current[i] = input[i];

        std::vector<double> injected_current(neurons.size(), 0.0);
        for (auto& syn : synapses) {
            double cur = syn.update_buffer();
            if (cur != 0.0) injected_current[syn.post] += cur;
        }

        std::vector<int> fired;
        for (int i = 0; i < (int)neurons.size(); ++i) {
            Neuron& n = neurons[i];
            if (n.refractory && (current_time - n.last_spike) < REFRACTORY) continue;
            n.refractory = false;
            if (n.v >= V_TH) {
                n.v = V_RESET;
                n.last_spike = current_time;
                n.refractory = true;
                fired.push_back(i);
            }
        }

        for (int pre : fired) {
            for (int syn_idx : post_synapses[pre]) {
                MSFSynapse& syn = synapses[syn_idx];
                if (V_TH >= syn.threshold) {
                    syn.add_spike(syn.weight * PULSE_AMP);
                    syn.trace_pre = 1.0;
                    syn.eligibility += A_PLUS * syn.trace_pre - A_MINUS * syn.trace_post;
                }
            }
        }

        std::vector<double> new_hidden_spikes(n_hidden, 0.0);
        for (int i = 0; i < (int)neurons.size(); ++i) {
            Neuron& n = neurons[i];
            if (n.refractory && (current_time - n.last_spike) < REFRACTORY) continue;
            if (n.refractory && (current_time - n.last_spike) >= REFRACTORY)
                n.refractory = false;
            double I = ext_current[i] + injected_current[i];
            double dv = (-(n.v - V_REST) + I) / TAU_M * DT;
            n.v += dv;
            if (n.v > V_MAX) n.v = V_MAX;
            if (n.v < V_REST) n.v = V_REST;
            if (i >= n_input && i < n_input + n_hidden) {
                new_hidden_spikes[i - n_input] = (n.v >= V_TH) ? 1.0 : 0.0;
            }
        }
        hidden_spikes = new_hidden_spikes;

        for (auto& syn : synapses) {
            syn.trace_pre *= std::exp(-DT / TAU_STDP);
            syn.trace_post *= std::exp(-DT / TAU_STDP);
            syn.eligibility *= std::exp(-DT / TAU_STDP);
        }

        for (int post : fired) {
            for (int i = 0; i < (int)neurons.size(); ++i) {
                for (int syn_idx : post_synapses[i]) {
                    if (synapses[syn_idx].post == post)
                        synapses[syn_idx].trace_post = 1.0;
                }
            }
        }
        current_time += DT;

        std::vector<double> out(n_output);
        for (int i = 0; i < n_output; ++i)
            out[i] = std::max(0.0, neurons[n_input + n_hidden + i].v / V_MAX);
        return out;
    }

    std::vector<double> get_hidden_spikes() const { return hidden_spikes; }

    void apply_rstdp(double reward) {}

    std::vector<double> get_weights() const {
        std::vector<double> w;
        for (auto& s : synapses) w.push_back(s.weight);
        return w;
    }

    void set_weights(const std::vector<double>& w) {
        for (size_t i = 0; i < synapses.size() && i < w.size(); ++i)
            synapses[i].weight = w[i];
    }

    int get_num_weights() const { return static_cast<int>(synapses.size()); }
};

// ======================== 机器人结构 ========================
class Robot {
public:
    SNN brain_net, comm_net;
    SNN explore_net;

    int x, y;
    int team;
    int id;
    double energy;
    int attack_cooldown;
    double score;
    bool alive;
    int steps_without_energy;
    double last_energy;
    int last_x, last_y;
    int explore_weight_update_counter;
    std::vector<double> last_comm_output;
    double last_strategy;

    mutable double last_max_energy;
    mutable double last_sensor0;
    mutable std::vector<double> last_sensors;

    std::vector<double> prev_brain_hidden;

    Robot(int robot_id, int team_id)
        : brain_net(BRAIN_IN, BRAIN_HIDDEN, BRAIN_OUT),
          comm_net(COMM_IN, COMM_HIDDEN, COMM_OUT),
          explore_net(EXPLORE_IN, EXPLORE_HIDDEN, EXPLORE_OUT),
          id(robot_id), team(team_id),
          steps_without_energy(0),
          explore_weight_update_counter(0),
          last_comm_output(2, 0.5),
          last_max_energy(0.0),
          last_sensor0(0.0),
          last_sensors(25, 0.0),
          prev_brain_hidden(BRAIN_HIDDEN, 0.0),
          last_strategy(0) {
        reset();
    }

    void reset() {
        energy = INIT_ENERGY;
        attack_cooldown = 0;
        score = 0.0;
        alive = true;
        last_x = -1; last_y = -1;
        last_energy = 0.0;
        steps_without_energy = 0;
        last_comm_output = {0.5, 0.5};
        last_max_energy = 0.0;
        last_sensor0 = 0.0;
        std::fill(last_sensors.begin(), last_sensors.end(), 0.0);
        std::fill(prev_brain_hidden.begin(), prev_brain_hidden.end(), 0.0);
        last_strategy = 0;
        brain_net.reset();
        comm_net.reset();
        explore_net.reset();
    }

    std::vector<double> get_sensor_input(const std::vector<Robot>& teammates,
                                         const std::vector<Robot>& enemies,
                                         const std::vector<std::pair<int,int>>& resource_positions,
                                         const std::vector<double>& resource_energy_map,
                                         double teammate_comm) const {
        std::vector<double> sensors(25, 0.0);
        double max_energy = 0.0;
        for (int dir = 0; dir < 8; ++dir) {
            int nx = x + DX[dir];
            int ny = y + DY[dir];
            if (nx >= 0 && nx < GRID_SIZE && ny >= 0 && ny < GRID_SIZE) {
                double raw = resource_energy_map[ny * GRID_SIZE + nx];
                sensors[dir] = raw / 2.0;
                if (sensors[dir] > max_energy) max_energy = sensors[dir];
            } else {
                sensors[dir] = 0.0;
            }
        }
        if (max_energy < EXPLORE_ENERGY_THRESHOLD) {
            const_cast<Robot*>(this)->steps_without_energy++;
        } else {
            const_cast<Robot*>(this)->steps_without_energy = 0;
        }
        for (int dir = 0; dir < 8; ++dir) {
            int nx = x + DX[dir];
            int ny = y + DY[dir];
            sensors[8+dir] = 0.0;
            for (const auto& enemy : enemies) {
                if (enemy.alive && enemy.x == nx && enemy.y == ny) {
                    sensors[8+dir] = 1.0;
                    break;
                }
            }
        }
        sensors[16] = x / (double)(GRID_SIZE - 1);
        sensors[17] = y / (double)(GRID_SIZE - 1);
        double min_dist = 1e9;
        int target_dx = 0, target_dy = 0;
        for (const auto& res : resource_positions) {
            int dx = res.first - x;
            int dy = res.second - y;
            double dist = std::abs(dx) + std::abs(dy);
            if (dist < min_dist) {
                min_dist = dist;
                target_dx = dx;
                target_dy = dy;
            }
        }
        if (min_dist < 1e8) {
            sensors[18] = target_dx / (double)GRID_SIZE * 2.0;
            sensors[19] = target_dy / (double)GRID_SIZE * 2.0;
            if (sensors[18] > 1.0) sensors[18] = 1.0;
            if (sensors[18] < -1.0) sensors[18] = -1.0;
            if (sensors[19] > 1.0) sensors[19] = 1.0;
            if (sensors[19] < -1.0) sensors[19] = -1.0;
        }
        sensors[20] = std::min(2.0, (energy / INIT_ENERGY) * 2.0);
        sensors[21] = (attack_cooldown > 0) ? 0.0 : 1.0;
        sensors[22] = teammate_comm;
        sensors[23] = 0.0;
        sensors[24] = 1.0 - std::exp(-steps_without_energy / 20.0);
        for (int i = 0; i < 24; ++i) {
            sensors[i] += (rand_uniform() - 0.5) * 0.02;
            if (i >= 8 && i < 16) {
                if (sensors[i] < 0.0) sensors[i] = 0.0;
                if (sensors[i] > 1.0) sensors[i] = 1.0;
            } else if (i == 21 || i == 22 || i == 23) {
                if (sensors[i] < 0.0) sensors[i] = 0.0;
                if (sensors[i] > 1.0) sensors[i] = 1.0;
            } else if (i == 24) {
                if (sensors[i] < 0.0) sensors[i] = 0.0;
                if (sensors[i] > 1.0) sensors[i] = 1.0;
            } else if (i == 18 || i == 19) {
                if (sensors[i] > 1.0) sensors[i] = 1.0;
                if (sensors[i] < -1.0) sensors[i] = -1.0;
            } else {
                if (sensors[i] < 0.0) sensors[i] = 0.0;
            }
        }
        if (sensors[24] < 0.0) sensors[24] = 0.0;
        if (sensors[24] > 1.0) sensors[24] = 1.0;

        last_max_energy = max_energy;
        last_sensor0 = sensors[0];
        last_sensors = sensors;
        return sensors;
    }

    void update_networks(const std::vector<double>& sensors,
                         double teammate_comm,
                         const std::vector<Robot>& enemies,
                         std::vector<double>& brain_out,
                         std::vector<double>& comm_out) {
        std::vector<double> brain_input = sensors;
        brain_input.push_back(teammate_comm);
        brain_input.push_back(0.0);
        brain_input.insert(brain_input.end(), prev_brain_hidden.begin(), prev_brain_hidden.end());
        for (int step = 0; step < 80; ++step)
            brain_out = brain_net.update(brain_input);
        int strategy = 0;
        double max_val = brain_out[0];
        for (int i = 1; i < BRAIN_OUT; ++i) {
            if (brain_out[i] > max_val) {
                max_val = brain_out[i];
                strategy = i;
            }
        }
        last_strategy = strategy;
        prev_brain_hidden = brain_net.get_hidden_spikes();

        std::vector<double> comm_input(COMM_IN);
        comm_input[0] = energy / INIT_ENERGY;
        comm_input[1] = sensors[18];
        comm_input[2] = sensors[19];
        comm_input[3] = sensors[8];
        for (int step = 0; step < 80; ++step)
            comm_out = comm_net.update(comm_input);
        last_comm_output = comm_out;
    }

    void execute_strategy(int strategy, int& move_dir, bool& attack,
                          double energy_ratio, double no_energy_norm) {
        attack = false;
        move_dir = 0;

        int best_dir = 0;
        double best_val = last_sensors[0];
        for (int d = 1; d < 8; ++d) {
            if (last_sensors[d] > best_val) {
                best_val = last_sensors[d];
                best_dir = d;
            }
        }
        bool has_energy = (best_val > 0.2);

        bool enemy_adjacent = false;
        int enemy_dir = 0;
        for (int d = 0; d < 8; ++d) {
            if (last_sensors[8+d] > 0.5) {
                enemy_adjacent = true;
                enemy_dir = d;
                break;
            }
        }

        switch (strategy) {
            case 0:
                if (enemy_adjacent) { attack = true; move_dir = enemy_dir; }
                else if (has_energy) move_dir = best_dir;
                else move_dir = rand_int(0,7);
                break;
            case 1:
                if (has_energy) move_dir = best_dir;
                else if (enemy_adjacent) { attack = true; move_dir = enemy_dir; }
                else move_dir = rand_int(0,7);
                break;
            case 2:
                if (steps_without_energy > EXPLORE_ACTIVE_THRESHOLD) {
                    std::vector<double> explore_input = {energy_ratio, no_energy_norm};
                    std::vector<double> explore_out = explore_net.update(explore_input);
                    move_dir = (int)(explore_out[0] * 8) % 8;
                } else {
                    move_dir = rand_int(0,7);
                }
                break;
            case 3:
                if (enemy_adjacent) move_dir = (enemy_dir + 4) % 8;
                else if (has_energy) move_dir = best_dir;
                else move_dir = rand_int(0,7);
                break;
            default: move_dir = rand_int(0,7);
        }
    }

    void decide_action(const std::vector<double>& brain_out,
                       int& move_dir, bool& attack,
                       double energy_ratio, double no_energy_norm) {
        explore_weight_update_counter++;
        if (explore_weight_update_counter >= 10) {
            explore_weight_update_counter = 0;
            std::vector<double> w = explore_net.get_weights();
            for (auto& weight : w) {
                weight += (rand_uniform() - 0.5) * 0.2;
                if (weight < 0.0) weight = 0.0;
                if (weight > 1.0) weight = 1.0;
            }
            explore_net.set_weights(w);
        }

        int strategy = (int)last_strategy;
        execute_strategy(strategy, move_dir, attack, energy_ratio, no_energy_norm);
    }

    void apply_rstdp(double reward) {}

    std::vector<double> get_all_weights() const {
        std::vector<double> w;
        auto w1 = brain_net.get_weights();
        auto w2 = comm_net.get_weights();
        auto w3 = explore_net.get_weights();
        w.insert(w.end(), w1.begin(), w1.end());
        w.insert(w.end(), w2.begin(), w2.end());
        w.insert(w.end(), w3.begin(), w3.end());
        return w;
    }

    void set_all_weights(const std::vector<double>& w) {
        int w1 = brain_net.get_num_weights();
        int w2 = comm_net.get_num_weights();
        int w3 = explore_net.get_num_weights();
        int idx = 0;
        brain_net.set_weights(std::vector<double>(w.begin(), w.begin() + w1));
        idx += w1;
        comm_net.set_weights(std::vector<double>(w.begin() + idx, w.begin() + idx + w2));
        idx += w2;
        explore_net.set_weights(std::vector<double>(w.begin() + idx, w.begin() + idx + w3));
    }

    int get_total_weights() const {
        return brain_net.get_num_weights() + comm_net.get_num_weights() + explore_net.get_num_weights();
    }
};

// ======================== 游戏环境 ========================
class Game {
private:
    std::vector<Robot> left_team, right_team;
    std::vector<std::pair<int,int>> resource_positions;
    std::vector<double> resource_energy_map;
    int step_count;
    bool demo_mode;
    double left_score, right_score;
    bool enable_rstdp;
    std::vector<double> last_comm_outputs_left;
    std::vector<double> last_comm_outputs_right;

    void update_resource_energy_map() {
        std::fill(resource_energy_map.begin(), resource_energy_map.end(), 0.0);
        for (const auto& res : resource_positions) {
            int rx = res.first, ry = res.second;
            for (int dy = -RESOURCE_DECAY_RADIUS; dy <= RESOURCE_DECAY_RADIUS; ++dy) {
                for (int dx = -RESOURCE_DECAY_RADIUS; dx <= RESOURCE_DECAY_RADIUS; ++dx) {
                    int nx = rx + dx, ny = ry + dy;
                    if (nx >= 0 && nx < GRID_SIZE && ny >= 0 && ny < GRID_SIZE) {
                        int dist = std::abs(dx) + std::abs(dy);
                        if (dist <= RESOURCE_DECAY_RADIUS) {
                            double energy = RESOURCE_ENERGY * std::pow(ENERGY_DECAY_FACTOR, dist);
                            int idx = ny * GRID_SIZE + nx;
                            resource_energy_map[idx] = std::max(resource_energy_map[idx], energy);
                        }
                    }
                }
            }
        }
    }

    bool is_position_occupied(int x, int y) const {
        for (const auto& r : left_team) if (r.alive && r.x == x && r.y == y) return true;
        for (const auto& r : right_team) if (r.alive && r.x == x && r.y == y) return true;
        return false;
    }

    void generate_resource_point() {
        int attempts = 0;
        while (attempts < 1000) {
            int x = rand_int(0, GRID_SIZE-1);
            int y = rand_int(0, GRID_SIZE-1);
            if (!is_position_occupied(x, y)) {
                resource_positions.push_back({x, y});
                return;
            }
            attempts++;
        }
        for (int y = 0; y < GRID_SIZE; ++y)
            for (int x = 0; x < GRID_SIZE; ++x)
                if (!is_position_occupied(x, y)) {
                    resource_positions.push_back({x, y});
                    return;
                }
    }

    void reset_resources() {
        resource_positions.clear();
        for (int i = 0; i < NUM_RESOURCES; ++i) generate_resource_point();
        update_resource_energy_map();
    }

public:
    Game(bool demo = false, bool rstdp = false)
        : demo_mode(demo), step_count(0), left_score(0), right_score(0),
          enable_rstdp(rstdp) {
        resource_energy_map.resize(GRID_SIZE * GRID_SIZE, 0.0);
        for (int i = 0; i < NUM_ROBOTS_PER_TEAM; ++i) {
            left_team.emplace_back(i, 0);
            right_team.emplace_back(i, 1);
        }
        last_comm_outputs_left.assign(NUM_ROBOTS_PER_TEAM * 2, 0.5);
        last_comm_outputs_right.assign(NUM_ROBOTS_PER_TEAM * 2, 0.5);
        reset();
    }

    void reset() {
        std::vector<std::pair<int,int>> positions;
        for (int i = 0; i < NUM_ROBOTS_PER_TEAM * 2; ++i) {
            int x, y;
            do {
                x = rand_int(0, GRID_SIZE-1);
                y = rand_int(0, GRID_SIZE-1);
            } while (std::find(positions.begin(), positions.end(), std::make_pair(x,y)) != positions.end());
            positions.push_back({x,y});
        }
        for (int i = 0; i < NUM_ROBOTS_PER_TEAM; ++i) {
            left_team[i].reset();
            left_team[i].x = positions[i].first;
            left_team[i].y = positions[i].second;
            left_team[i].alive = true;
            left_team[i].energy = INIT_ENERGY;
            left_team[i].score = 0.0;
            left_team[i].attack_cooldown = 0;

            right_team[i].reset();
            right_team[i].x = positions[NUM_ROBOTS_PER_TEAM + i].first;
            right_team[i].y = positions[NUM_ROBOTS_PER_TEAM + i].second;
            right_team[i].alive = true;
            right_team[i].energy = INIT_ENERGY;
            right_team[i].score = 0.0;
            right_team[i].attack_cooldown = 0;
        }
        reset_resources();
        for (auto& r : left_team) r.last_energy = resource_energy_map[r.y * GRID_SIZE + r.x];
        for (auto& r : right_team) r.last_energy = resource_energy_map[r.y * GRID_SIZE + r.x];
        std::fill(last_comm_outputs_left.begin(), last_comm_outputs_left.end(), 0.5);
        std::fill(last_comm_outputs_right.begin(), last_comm_outputs_right.end(), 0.5);
        step_count = 0;
        left_score = 0;
        right_score = 0;
    }

    void set_left_weights(const std::vector<double>& all_weights) {
        int w_per_robot = left_team[0].get_total_weights();
        for (int i = 0; i < NUM_ROBOTS_PER_TEAM; ++i) {
            std::vector<double> w(all_weights.begin() + i * w_per_robot,
                                  all_weights.begin() + (i+1) * w_per_robot);
            left_team[i].set_all_weights(w);
        }
    }

    void set_right_weights(const std::vector<double>& all_weights) {
        int w_per_robot = right_team[0].get_total_weights();
        for (int i = 0; i < NUM_ROBOTS_PER_TEAM; ++i) {
            std::vector<double> w(all_weights.begin() + i * w_per_robot,
                                  all_weights.begin() + (i+1) * w_per_robot);
            right_team[i].set_all_weights(w);
        }
    }

    bool step() {
        if (step_count >= DEMO_STEPS) return false;

        std::vector<Robot*> all_robots;
        for (auto& r : left_team) if (r.alive) all_robots.push_back(&r);
        for (auto& r : right_team) if (r.alive) all_robots.push_back(&r);
        if (all_robots.empty()) return false;

        std::vector<std::vector<double>> current_comm_outs(all_robots.size(), std::vector<double>(2, 0.5));
        std::vector<int> move_dirs(all_robots.size());
        std::vector<bool> attacks(all_robots.size());

        #pragma omp parallel for
        for (size_t i = 0; i < all_robots.size(); ++i) {
            Robot* r = all_robots[i];
            std::vector<Robot> teammates, enemies;
            if (r->team == 0) {
                for (auto& t : left_team) if (t.alive && t.id != r->id) teammates.push_back(t);
                for (auto& e : right_team) if (e.alive) enemies.push_back(e);
            } else {
                for (auto& t : right_team) if (t.alive && t.id != r->id) teammates.push_back(t);
                for (auto& e : left_team) if (e.alive) enemies.push_back(e);
            }

            double teammate_comm = 0.0;
            if (r->team == 0) {
                int other_id = (r->id == 0) ? 1 : 0;
                teammate_comm = last_comm_outputs_left[other_id * 2];
            } else {
                int other_id = (r->id == 0) ? 1 : 0;
                teammate_comm = last_comm_outputs_right[other_id * 2];
            }

            auto sensors = r->get_sensor_input(teammates, enemies, resource_positions, resource_energy_map, teammate_comm);
            std::vector<double> brain_out, comm_out;
            r->update_networks(sensors, teammate_comm, enemies, brain_out, comm_out);
            current_comm_outs[i] = comm_out;

            int move_dir;
            bool attack;
            double energy_ratio = r->energy / INIT_ENERGY;
            double no_energy_norm = std::min(1.0, r->steps_without_energy / 100.0);
            r->decide_action(brain_out, move_dir, attack, energy_ratio, no_energy_norm);
            move_dirs[i] = move_dir;
            attacks[i] = attack;
        }

        for (size_t i = 0; i < all_robots.size(); ++i) {
            Robot* r = all_robots[i];
            if (r->team == 0) {
                last_comm_outputs_left[r->id * 2] = current_comm_outs[i][0];
                last_comm_outputs_left[r->id * 2 + 1] = current_comm_outs[i][1];
            } else {
                last_comm_outputs_right[r->id * 2] = current_comm_outs[i][0];
                last_comm_outputs_right[r->id * 2 + 1] = current_comm_outs[i][1];
            }
        }

        // 移动逻辑
        for (size_t i = 0; i < all_robots.size(); ++i) {
            Robot* r = all_robots[i];
            int dir = move_dirs[i];
            int nx = r->x + DX[dir];
            int ny = r->y + DY[dir];
            bool moved = false;
            if (nx >= 0 && nx < GRID_SIZE && ny >= 0 && ny < GRID_SIZE) {
                bool occupied = false;
                for (auto& other : all_robots) {
                    if (other != r && other->alive && other->x == nx && other->y == ny) {
                        occupied = true;
                        break;
                    }
                }
                if (!occupied) {
                    double old_energy = r->last_energy;
                    double new_energy = resource_energy_map[ny * GRID_SIZE + nx];
                    double energy_delta = new_energy - old_energy;
                    const double ENERGY_GAIN_REWARD = 2.0;
                    const double ENERGY_LOSS_PENALTY = 1.0;
                    if (energy_delta > 0) r->score += energy_delta * ENERGY_GAIN_REWARD;
                    else if (energy_delta < 0) r->score += energy_delta * ENERGY_LOSS_PENALTY;
                    r->x = nx; r->y = ny;
                    r->last_energy = new_energy;
                    r->score += MOVE_REWARD;
                    moved = true;
                }
            }
            if (!moved) r->score -= 0.02;
            r->last_x = r->x; r->last_y = r->y;
        }

        // 攻击逻辑
        for (size_t i = 0; i < all_robots.size(); ++i) {
            Robot* attacker = all_robots[i];
            if (attacks[i] && attacker->attack_cooldown == 0) {
                for (auto& target : all_robots) {
                    if (target->team != attacker->team && target->alive &&
                        std::abs(attacker->x - target->x) + std::abs(attacker->y - target->y) == 1) {
                        attacker->attack_cooldown = ATTACK_COOLDOWN_MAX;
                        target->energy -= ATTACK_DAMAGE;
                        attacker->score += ATTACK_REWARD;
                        if (target->energy <= 0) {
                            target->alive = false;
                            target->energy = 0;
                            target->score += DEATH_PENALTY;
                        }
                        break;
                    }
                }
            }
            if (attacker->attack_cooldown > 0) attacker->attack_cooldown--;
        }

        // 资源采集
        for (size_t i = 0; i < resource_positions.size(); ++i) {
            int rx = resource_positions[i].first, ry = resource_positions[i].second;
            bool collected = false;
            for (auto& r : left_team) {
                if (r.alive && r.x == rx && r.y == ry) {
                    r.score += COLLECT_REWARD;
                    r.energy = std::min(INIT_ENERGY, r.energy + 30.0);
                    r.steps_without_energy = 0;
                    collected = true;
                    break;
                }
            }
            if (!collected) {
                for (auto& r : right_team) {
                    if (r.alive && r.x == rx && r.y == ry) {
                        r.score += COLLECT_REWARD;
                        r.energy = std::min(INIT_ENERGY, r.energy + 30.0);
                        r.steps_without_energy = 0;
                        collected = true;
                        break;
                    }
                }
            }
            if (collected) {
                resource_positions.erase(resource_positions.begin() + i);
                generate_resource_point();
                update_resource_energy_map();
                i--;
            }
        }

        left_score = 0; right_score = 0;
        for (auto& r : left_team) left_score += r.score;
        for (auto& r : right_team) right_score += r.score;

        step_count++;
        return true;
    }

    void display_ascii() const {
        char grid[GRID_SIZE][GRID_SIZE];
        for (int y = 0; y < GRID_SIZE; ++y)
            for (int x = 0; x < GRID_SIZE; ++x) grid[y][x] = '.';
        for (const auto& res : resource_positions) {
            int x = res.first, y = res.second;
            if (x>=0 && x<GRID_SIZE && y>=0 && y<GRID_SIZE) grid[y][x] = '#';
        }
        for (const auto& r : left_team) {
            if (r.alive && r.x>=0 && r.x<GRID_SIZE && r.y>=0 && r.y<GRID_SIZE)
                grid[r.y][r.x] = 'L';
        }
        for (const auto& r : right_team) {
            if (r.alive && r.x>=0 && r.x<GRID_SIZE && r.y>=0 && r.y<GRID_SIZE)
                grid[r.y][r.x] = 'R';
        }
        std::cout << "\n  ";
        for (int x = 0; x < GRID_SIZE; ++x) std::cout << (x%10) << " ";
        std::cout << "\n  +" << std::string(GRID_SIZE*2, '-') << "+\n";
        for (int y = GRID_SIZE-1; y >= 0; --y) {
            std::cout << (y%10) << " |";
            for (int x = 0; x < GRID_SIZE; ++x) {
                std::cout << grid[y][x] << " ";
            }
            std::cout << "|\n";
        }
        std::cout << "  +" << std::string(GRID_SIZE*2, '-') << "+\n";
        std::cout << "Step: " << step_count << " | Left Score: " << left_score << " | Right Score: " << right_score;
        if (enable_rstdp) std::cout << " [R-STDP ON]";
        std::cout << std::endl;
        std::cout << "Debug: ";
        for (const auto& r : left_team) {
            if (r.alive) {
                std::cout << "L" << r.id << ":strat=" << (int)r.last_strategy
                          << " hungry=" << r.steps_without_energy
                          << (r.steps_without_energy > EXPLORE_ACTIVE_THRESHOLD ? "[EXP]" : "") << " ";
            }
        }
        for (const auto& r : right_team) {
            if (r.alive) {
                std::cout << "R" << r.id << ":strat=" << (int)r.last_strategy
                          << " hungry=" << r.steps_without_energy
                          << (r.steps_without_energy > EXPLORE_ACTIVE_THRESHOLD ? "[EXP]" : "") << " ";
            }
        }
        std::cout << std::endl;
    }

    void demo_run(const std::vector<double>& best_left, const std::vector<double>& best_right) {
        set_left_weights(best_left);
        set_right_weights(best_right);
        reset();
        while (step() && step_count < DEMO_STEPS) {
#ifdef _WIN32
            system("cls");
#else
            system("clear");
#endif
            display_ascii();
            std::this_thread::sleep_for(std::chrono::milliseconds(100));
        }
    }
};

// ======================== 主函数 ========================
int main(int argc, char* argv[]) {
    std::vector<double> best_left, best_right;
    std::ifstream fin_left("best_left.txt"), fin_right("best_right.txt");
    if (!fin_left || !fin_right) {
        std::cerr << "Error: best_left.txt or best_right.txt not found. Train first." << std::endl;
        return 1;
    }
    double val;
    while (fin_left >> val) best_left.push_back(val);
    while (fin_right >> val) best_right.push_back(val);
    fin_left.close(); fin_right.close();
    Game game(true, false);   // demo_mode = true, rstdp = false
    game.demo_run(best_left, best_right);
    return 0;
}