Reliable Critics: Monotonic Improvement and Convergence Guarantees for Reinforcement Learning
Eshwar S. R., Gugan Thoppe, Aditya Gopalan, Gal Dalal·June 08, 2025
Summary
RPI enhances reinforcement learning, ensuring monotonic improvement and convergence. It uses Bellman-based constrained optimization for policy evaluation, offering value estimate lower bounds. Integrating RPI into DQN and DDPG algorithms boosts performance. In classical control tasks, RPI methods outperform baselines. RPIDDPG and RPIDQN algorithms, with critic and actor initialization, minimize critic loss and apply policy gradient. They use replay buffers for updates. RPIDQN, with StableBaselines3 hyperparameters, excels in CartPole evaluations, providing lower-bounded estimates. PPO offers accurate estimates. TD3, in the Inverted Pendulum task, also shows lower-bounded estimates, unlike DDPG's overestimation. Adjustments to the critic loss function reduce overestimation in both algorithms.
Introduction
Background
Overview of reinforcement learning
Importance of monotonic improvement and convergence in RL
Objective
Purpose of RPI in reinforcement learning
Key benefits of using RPI in policy evaluation
Method
Bellman-based Constrained Optimization
Explanation of Bellman equations in reinforcement learning
How RPI employs constrained optimization for policy evaluation
Value estimate lower bounds provided by RPI
Integration into DQN and DDPG Algorithms
Description of DQN and DDPG algorithms
How RPI is integrated into these algorithms to enhance performance
Specific modifications and benefits in DQN and DDPG
Classical Control Tasks
Overview of classical control tasks in reinforcement learning
Comparison of RPI methods against baselines in these tasks
Key performance indicators and outcomes
RPIDDPG and RPIDQN Algorithms
Detailed explanation of RPIDDPG and RPIDQN
Critic and actor initialization strategies
Role of replay buffers in algorithm updates
Hyperparameters and performance in CartPole evaluations
RPIDQN with StableBaselines3
Description of StableBaselines3 and its role in RPIDQN
Evaluation of RPIDQN in CartPole tasks
Lower-bounded estimates and performance metrics
PPO and TD3 Algorithms
Overview of PPO and TD3 algorithms
Comparison of RPI methods with these algorithms in specific tasks
Lower-bounded estimates in Inverted Pendulum task for TD3
Adjustments to the critic loss function to reduce overestimation
Conclusion
Summary of RPI's impact on reinforcement learning
Future directions and potential applications
Key takeaways and implications for the field
Basic info
papers
optimization and control
machine learning
artificial intelligence
Advanced features
Insights
How does RPI address the overestimation bias commonly observed in algorithms like DDPG, and what adjustments are made to the critic loss function?
What are the key steps involved in integrating RPI into DQN and DDPG algorithms, including initialization and updates?
In which classical control tasks do RPI-enhanced methods (RPIDQN, RPIDDPG) demonstrate superior performance compared to baseline algorithms like PPO and TD3?
How does RPI ensure monotonic improvement and convergence in reinforcement learning algorithms?