Adaptive pessimism via target Q-value for offline reinforcement learning.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Offline reinforcement learning (RL) learns from fixed datasets without environment interaction.
• Out-of-distribution (OOD) actions in datasets can lead to significant errors in Q-value estimation.
• Existing methods for handling OOD actions often suffer from insufficient or excessive pessimism, hindering policy learning.

Purpose of the Study:

• To develop a novel offline RL algorithm that adaptively balances pessimism constraints with standard RL objectives.
• To address the challenge of varying data distributions across different tasks and behavior policies.
• To improve the stability and performance of policy learning in offline RL settings.

Main Methods:

• Introduced the concept of using Q-value quantiles to represent the distribution of values within a fixed dataset.
• Designed the Adaptive Pessimism via Target Q-value (APTQ) algorithm.
• APTQ adaptively balances pessimism constraints and the RL objective by targeting a Q-value quantile.

Main Results:

• The proposed APTQ algorithm demonstrated improved performance over state-of-the-art methods.
• Achieved a 6.20% performance improvement on the D4RL-v0 benchmark.
• Showcased a 1.89% performance improvement on the D4RL-v2 benchmark.

Conclusions:

• Q-value quantiles are effective for understanding value distributions in offline RL datasets.
• APTQ provides a stable and adaptive approach to balancing pessimism and RL objectives.
• The method significantly enhances policy learning performance in offline RL tasks.