De-Pessimism Offline Reinforcement Learning via Value Compensation.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Offline reinforcement learning (RL) offers efficient data utilization but suffers from policy deviation vulnerabilities.
• Existing methods often employ pessimism through policy constraints or conservative Q-value estimation, leading to suboptimal policies.

Purpose of the Study:

• To address the pessimism problem in offline RL by developing accurate Q-value estimation techniques.
• To mitigate suboptimal policy learning caused by overly conservative approaches in offline RL.

Main Methods:

• Propose a de-pessimism (DEP) operator for Q-value estimation, utilizing optimal Bellman or compensation operators based on action distribution.
• Introduce a compensation operator to assess out-of-distribution (OOD) actions and adjust Q-values using state-value differences, alleviating pessimism.
• Integrate the DEP operator into the Soft Actor-Critic (SAC) algorithm to create the value-compensated de-pessimism offline RL (DoRL-VC) framework.

Main Results:

• Theoretical demonstration of DEP operator's convergence and effectiveness in policy improvement.
• Empirical validation showing DoRL-VC achieving state-of-the-art (SOTA) performance on locomotion, Maze 2-D, and Adroit tasks.
• Evidence of DEP's efficacy in mitigating pessimism and enhancing policy performance in practical offline RL scenarios.

Conclusions:

• The proposed de-pessimism (DEP) operator effectively addresses the pessimism challenge in offline RL.
• Value-compensated de-pessimism offline RL (DoRL-VC) achieves SOTA results, demonstrating the practical benefits of mitigating pessimism.
• Accurate Q-value estimation is crucial for improving policy performance in data-efficient offline reinforcement learning.