Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning
This work addresses sample efficiency issues in reinforcement learning for high-dimensional action spaces, representing an incremental improvement over existing methods.
The paper tackles the problem of large bias in high-dimensional reinforcement learning tasks caused by standard importance sampling weight clipping in PPO, proposing dimension-wise clipping to reduce bias and enable sample reuse. The proposed algorithm outperforms PPO and other methods in OpenAI Gym tasks.
In importance sampling (IS)-based reinforcement learning algorithms such as Proximal Policy Optimization (PPO), IS weights are typically clipped to avoid large variance in learning. However, policy update from clipped statistics induces large bias in tasks with high action dimensions, and bias from clipping makes it difficult to reuse old samples with large IS weights. In this paper, we consider PPO, a representative on-policy algorithm, and propose its improvement by dimension-wise IS weight clipping which separately clips the IS weight of each action dimension to avoid large bias and adaptively controls the IS weight to bound policy update from the current policy. This new technique enables efficient learning for high action-dimensional tasks and reusing of old samples like in off-policy learning to increase the sample efficiency. Numerical results show that the proposed new algorithm outperforms PPO and other RL algorithms in various Open AI Gym tasks.