Not All Transitions Matter: Evidence from PPO
On-policy reinforcement learning suffers from hidden training instability caused by redundant gradient signals from causally chained, non-independent transitions in rollouts. This paper demonstrates that simply dropping a fixed fraction (e.g., 25%) of these transitions during sampling breaks the repetitive gradient structure, stabilizing training without altering the core algorithm. The method matches standard PPO in final reward performance across five environments while yielding more consisten
Deep Analysis
Background
Training an on-policy reinforcement learning (RL) agent like Proximal Policy Optimization (PPO) requires collecting fresh experience at every update. However, the sequential nature of this data collection creates a fundamental issue. Each state in a rollout is causally dependent on the previous state and the agent's own action, meaning consecutive transitions are not independent samples. This inherent correlation causes the gradient signals to become highly repetitive, as the same informative directions are reinforced over and over. While not always visible in final reward curves, this redundancy can destabilize training by, for instance, causing the value network to lag behind a rapidly shifting policy.
Key Points
The core innovation is a minimal, preprocessing-level intervention to mitigate this redundancy.
- The Solution: Randomly dropping a fixed fraction of transitions from a collected rollout before using it for updates. Crucially, this occurs at the right stage so the reward signal (the RL objective) remains intact.
- Mechanism: By removing transitions, the method breaks the repetitive gradient structure caused by correlated data. It forces the learning process to generalize from a less redundant set of updates.
- Implementation Simplicity: The change requires only one extra sampling step during batch preparation. It introduces no new components, modifies no core PPO algorithmic steps, and is compatible with any standard PPO implementation.
- Experimental Validation: The method was tested on five environments of increasing difficulty (CartPole-v1, Acrobot-v1, LunarLander-v2, HalfCheetah-v5, Hopper-v5). Results show it matches vanilla PPO in final reward performance while generating more consistent training dynamics across key metrics like KL divergence, policy entropy, and value loss estimates.
- Optimal Hyperparameter: Dropping 25% of transitions emerged as the "sweet spot"—sufficient to disrupt redundancy without excessively thinning the training batch.
Significance
This work addresses a "sneaky instability" in on-policy RL that is often overlooked because its symptoms are masked by final performance metrics. The proposed solution is significant for several reasons:
- Simplicity and Practicality: It offers a low-cost, easy-to-implement fix that can be directly integrated into existing PPO codebases, making it immediately useful for practitioners.
- Conceptual Insight: It provides a clear mechanistic explanation for training instability and shows that intentionally introducing a form of data loss (dropping transitions) can improve learning stability—a counterintuitive but effective strategy.
- Robustness: The empirical success across diverse environments (control tasks with varying dynamics) suggests the method targets a fundamental aspect of on-policy data, not a problem specific to one domain. It improves the reliability and consistency of the training process, which is critical for reproducible research and stable deployment.
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