Off-Policy RL Replay Buffer Memory Leak: Fix 2M Step Crash

#sac #td3 #dqn #replaybuffer

The Silent Killer That Crashes Your SAC Agent at 2AM

Your off-policy RL agent trains beautifully for 1.5 million steps. Loss curves look good, rewards climbing steadily. Then somewhere around step 2 million, the process dies with MemoryError or gets quietly killed by the OOM daemon. No warning, no gradual slowdown — just a hard crash.

I've debugged this exact scenario three times in the past year. The culprit is almost always the replay buffer, and the leak isn't where you'd expect.

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Why Off-Policy Algorithms Are Memory Time Bombs

Off-policy RL methods like SAC, TD3, and DQN depend on experience replay — storing past transitions in a buffer and sampling from them during training. This breaks the temporal correlation in on-policy methods like PPO and dramatically improves sample efficiency.

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