Variational Delayed Policy Optimization
Qingyuan Wu, Simon Sinong Zhan, Yixuan Wang, Yuhui Wang, Chung-Wei Lin, Chen Lv, Qi Zhu, Chao Huang·May 23, 2024
Summary
The paper introduces Variational Delayed Policy Optimization (VDPO), a reinforcement learning framework that addresses the challenge of observation delay in environments. VDPO reformulates the problem as a variational inference task, combining Temporal Difference (TD) learning for a reference policy in a delay-free MDP and behavior cloning for delayed decision-making. It improves sample efficiency by approximately 50% compared to state-of-the-art methods, such as A-SAC, DC/AC, DIDA, BPQL, and AD-SAC, as demonstrated in MuJoCo benchmark tasks. VDPO combines a model-based approach with a two-head transformer to learn a belief estimator and policy, showing robust performance across various tasks with constant and stochastic delays. The algorithm's effectiveness is supported by empirical results and theoretical analysis, making it a promising method for handling delayed reinforcement learning problems.
Introduction
Background
[ ] Overview of observation delay challenges in reinforcement learning
[ ] Importance of handling delays for real-world applications
Objective
[ ] To develop a novel framework that addresses observation delay
[ ] Improve sample efficiency compared to existing methods
[ ] Achieve robust performance across various tasks
Method
Problem Formulation
[ ] Variational inference approach to reformulate the delayed MDP
Temporal Difference (TD) Learning
[ ] Reference policy and delay-free MDP
[ ] TD updates for the belief estimator
Behavior Cloning for Delayed Decision-Making
[ ] Learning policy for delayed actions using behavior cloning
Model-Based Component
[ ] Two-head transformer for belief estimation
[ ] Combining model predictions with observed transitions
Algorithm Description
[ ] VDPO algorithm steps: pretraining, belief update, policy optimization
Sample Efficiency Analysis
[ ] Empirical results comparing VDPO to state-of-the-art methods (A-SAC, DC/AC, DIDA, BPQL, AD-SAC)
Robustness Evaluation
[ ] Performance across tasks with constant and stochastic delays
Theoretical Analysis
[ ] Justification of the algorithm's effectiveness through theoretical grounding
Experimental Results
MuJoCo Benchmark Tasks
[ ] Performance comparison in different environments
[ ] Quantitative improvements in learning speed and cumulative reward
Real-world Applications
[ ] Case studies showcasing VDPO's applicability
Conclusion
[ ] Summary of VDPO's contributions
[ ] Limitations and future research directions
[ ] Potential impact on the reinforcement learning community
Basic info
papers
machine learning
artificial intelligence
Advanced features
Insights
How does VDPO combine a model-based approach to handle delayed decision-making in MuJoCo benchmark tasks?
What is the primary focus of the Variational Delayed Policy Optimization (VDPO) paper?
How does VDPO address the observation delay issue in reinforcement learning environments?
What improvement in sample efficiency does VDPO achieve compared to state-of-the-art methods like A-SAC, DC/AC, DIDA, BPQL, and AD-SAC?