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openai.com·14h ago
AI Research
arXiv CS.AI·May 26·5 min read
Not All Transitions Matter: Evidence from PPO
TL;DR
Reinforcement learning agents often face issues due to the redundancy in consecutive transitions, which can lead to unstable training. This paper introduces a method to randomly drop 25% of transitions during training, effectively breaking the repetitive gradient structure.
✦ Why It Matters
Engineers can implement this transition-dropping technique to improve the stability of their reinforcement learning models without significant changes to existing algorithms.
Key Takeaways
- Consider dropping 25% of transitions during training.
- Monitor training dynamics for stability improvements.
- Apply this method across various reinforcement learning environments.
- No need to modify the core PPO algorithm.
Full Summary
In reinforcement learning, particularly with on-policy methods like Proximal Policy Optimization (PPO), agents collect experiences that are causally linked, leading to overlapping information and repetitive gradient signals. This redundancy can destabilize training, making it difficult for the value network to adapt to policy changes.
The authors propose a simple yet effective technique: randomly dropping a fixed fraction of transitions—specifically 25%—from the training data. This adjustment disrupts the repetitive nature of the gradients without altering the core PPO algorithm.
Experiments across five environments, including CartPole and LunarLander, show that this method matches the reward performance of standard PPO while yielding more stable training dynamics in terms of KL divergence and policy entropy. The findings suggest that reducing redundancy in training data can enhance the robustness of reinforcement learning agents.