**Dynamic Reward Adaptation with Reinforcement Learning** I

Dynamic Reward Adaptation with Reinforcement Learning

In traditional reinforcement learning (RL), an agent learns to optimize its behavior based on a fixed reward signal. However, in real-world environments, expectations and reward structures can shift over time. To address this challenge, we need an RL algorithm that can adapt to changing reward signals during deployment, ensuring stable performance despite evolving expectations.

Key Challenges:

1. Non-stationarity: The reward signal changes over time, making it difficult for the agent to learn a stable policy.
2. Uncertainty: The agent must handle uncertainty about the reward signal, such as noisy or delayed feedback.
3. Exploration-Exploitation Trade-off: The agent must balance exploring new actions with exploiting known good policies.

Proposed Solution:

We propose a Meta-RL approach, where the agent learns to adapt to changing reward signals using a secondary learning process. This secondary proc...

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