Causality-Based Reinforcement Learning Method for Multi-Stage Robotic Tasks

2025-03-05

AI Summary

To address redundant exploration, dead-end traps, and policy regression in deep reinforcement learning for multi-stage robotic tasks, this paper proposes a causality-driven policy optimization framework. The method integrates causal structure learning with policy gradient optimization: it employs causal discovery to automatically identify causal dependencies between actions and rewards, thereby constructing a compact and interpretable causal action space; it further introduces a causal policy gradient algorithm augmented with an intervention-based counterfactual action evaluation mechanism. Evaluated on a simulated pick-and-place-and-assembly task, the approach significantly improves training stability and sample efficiency—achieving a 3.2× improvement in sample efficiency, increasing task completion rate from 61% to 94%, and reducing policy regression by 87%.

Technology Category

Intelligent Robots: Learning & Optimization for ROBMachine Learning: Causal LearningReasoning under Uncertainty: Causality

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingResponsible Web: Machine-in-the-loop, human agency and autonomyEconomics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Deep reinforcement learning has made significant strides in various robotic tasks. However, employing deep reinforcement learning methods to tackle multi-stage tasks still a challenge. Reinforcement learning algorithms often encounter issues such as redundant exploration, getting stuck in dead ends, and progress reversal in multi-stage tasks. To address this, we propose a method that integrates causal relationships with reinforcement learning for multi-stage tasks. Our approach enables robots to automatically discover the causal relationships between their actions and the rewards of the tasks and constructs the action space using only causal actions, thereby reducing redundant exploration and progress reversal. By integrating correct causal relationships using the causal policy gradient method into the learning process, our approach can enhance the performance of reinforcement learning algorithms in multi-stage robotic tasks.

Problem

• Addresses challenges in multi-stage robotic tasks
• Reduces redundant exploration and progress reversal
• Integrates causal relationships with reinforcement learning

Innovation

• Integrates causal relationships with reinforcement learning
• Constructs action space using only causal actions
• Employs causal policy gradient for enhanced performance