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ACE : Off-Policy Actor-Critic with Causality-Aware Entropy Regularization (2402.14528v5)

Published 22 Feb 2024 in cs.LG and cs.AI

Abstract: The varying significance of distinct primitive behaviors during the policy learning process has been overlooked by prior model-free RL algorithms. Leveraging this insight, we explore the causal relationship between different action dimensions and rewards to evaluate the significance of various primitive behaviors during training. We introduce a causality-aware entropy term that effectively identifies and prioritizes actions with high potential impacts for efficient exploration. Furthermore, to prevent excessive focus on specific primitive behaviors, we analyze the gradient dormancy phenomenon and introduce a dormancy-guided reset mechanism to further enhance the efficacy of our method. Our proposed algorithm, ACE: Off-policy Actor-critic with Causality-aware Entropy regularization, demonstrates a substantial performance advantage across 29 diverse continuous control tasks spanning 7 domains compared to model-free RL baselines, which underscores the effectiveness, versatility, and efficient sample efficiency of our approach. Benchmark results and videos are available at https://ace-rl.github.io/.

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Authors (10)
  1. Tianying Ji (12 papers)
  2. Yongyuan Liang (18 papers)
  3. Yan Zeng (46 papers)
  4. Yu Luo (143 papers)
  5. Guowei Xu (15 papers)
  6. Jiawei Guo (16 papers)
  7. Ruijie Zheng (23 papers)
  8. Furong Huang (150 papers)
  9. Fuchun Sun (127 papers)
  10. Huazhe Xu (93 papers)
Citations (5)
View on Semantic Scholar

Summary

  • The paper introduces a novel causality-based modification to the actor-critic method that prioritizes primitive behaviors based on their impact on rewards.
  • It incorporates a modified entropy regularization term and a gradient-dormancy reset to enhance exploration efficiency and prevent overfitting.
  • Empirical results across 29 tasks demonstrate a 2.1-fold improvement on high-difficulty manipulator tasks and superior sample efficiency in sparse reward settings.

Insights into ACE: Off-Policy Actor-Critic with Causality-Aware Entropy Regularization

The paper presents an innovative reinforcement learning (RL) framework, ACE: Off-Policy Actor-Critic with Causality-Aware Entropy Regularization. This work addresses the oversight of prior RL models regarding the varying significance of primitive behaviors during policy learning by integrating causal inference techniques and advanced exploration mechanisms into the RL paradigm.

Overview of the Methodology

The cornerstone of this paper is the insight into the differential significance of primitive behaviors throughout the learning process. The authors introduce a novel causality-aware approach to off-policy actor-critic algorithms, leveraging the causal relationships between action dimensions and rewards. By incorporating a causality-aware entropy term, the proposed algorithm identifies and prioritizes actions that have a higher potential impact, thereby enhancing exploration efficiency.

  1. Causal Policy-Reward Structural Model: This model evaluates the influence of primitive behaviors by quantifying their causal impact on rewards. The authors establish a theoretical basis for the identifiability of causal structures in RL using this model.
  2. Causality-Aware Entropy Regularization: The authors propose a modified entropy term weighted by causal significance, which emphasizes exploration of actions with high importance at various learning stages. This is implemented within a maximum entropy RL framework.
  3. Gradient-Dormancy-Guided Reset: To circumvent the risk of overfitting to specific behaviors, the authors present a gradient dormancy-based reset mechanism. By monitoring dormant neurons within the network, this mechanism intermittently resets network weights according to the degrees of dormancy, thus maintaining network expressivity and enhancing exploration.

Empirical Evaluation

The algorithm demonstrates robust performance improvements across a suite of 29 diverse tasks spanning various domains, including tabletop manipulation, locomotion, and dexterous hand manipulation. Compared to the state-of-the-art model-free RL baselines like Soft Actor-Critic (SAC) and Twin Delayed DDPG (TD3), ACE consistently outperforms, particularly excelling in challenging high-dimensional tasks and sparse reward settings.

Notably, the implementation of ACE yielded:

  • A 2.1-fold improvement on high-difficulty manipulator tasks.
  • Enhanced sample efficiency, as evident from the successful completion of challenging sparse reward tasks, where traditional baselines notably failed.

Implications and Future Directions

Practically, the research presents a versatile, modular addition to model-free RL frameworks that can be employed to optimize exploration strategies through a causality-focused lens. Theoretically, the paper opens promising avenues for integrating causal inference methods into RL to uncover latent structures in action-reward dynamics.

Potential future research could explore the applications of ACE in more complex environments, such as those requiring long-horizon planning or involving non-stationary dynamics. Additionally, further exploration into scaling this methodology for real-time applications and reducing computational overhead will be valuable.

In conclusion, the paper provides a significant contribution to reinforcement learning by enriching the learning process with causal insights, thereby setting a foundation for more adaptive and efficient RL algorithms in varied real-world applications.

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