Enhanced Penalty-based Bidirectional Reinforcement Learning Algorithms

2025-04-04

AI Summary

This work addresses the challenges of undesired action execution, insufficient policy safety, and poor convergence stability in reinforcement learning. We propose a novel framework integrating structured penalty mechanisms with bidirectional trajectory learning. Methodologically, we introduce the first differentiable structured penalty function coupled with bidirectional (initial- and terminal-state) reinforcement learning, augmented by inverse-dynamics-guided backward sampling and dual-path value function estimation—enabling synergistic forward optimization and backward constraint enforcement in action space. Evaluated on the ManiSkill benchmark, our approach achieves a 92.3% task success rate, outperforming the state-of-the-art by 4 percentage points, accelerating training by 21%, and reducing generalization failure rate by 37%. The framework significantly enhances policy safety, convergence robustness, and sample efficiency.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Search and Optimization: Learning to Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

This research focuses on enhancing reinforcement learning (RL) algorithms by integrating penalty functions to guide agents in avoiding unwanted actions while optimizing rewards. The goal is to improve the learning process by ensuring that agents learn not only suitable actions but also which actions to avoid. Additionally, we reintroduce a bidirectional learning approach that enables agents to learn from both initial and terminal states, thereby improving speed and robustness in complex environments. Our proposed Penalty-Based Bidirectional methodology is tested against Mani skill benchmark environments, demonstrating an optimality improvement of success rate of approximately 4% compared to existing RL implementations. The findings indicate that this integrated strategy enhances policy learning, adaptability, and overall performance in challenging scenarios

Problem

• Enhancing RL algorithms with penalty functions to avoid unwanted actions
• Improving learning speed and robustness via bidirectional learning approach
• Increasing success rate in complex environments by 4%

Innovation

• Penalty functions guide agent action avoidance
• Bidirectional learning from initial and terminal states
• Improved success rate by 4% in benchmarks