Maximum Entropy On-Policy Actor-Critic via Entropy Advantage Estimation

Abstract: Entropy Regularisation is a widely adopted technique that enhances policy optimisation performance and stability. A notable form of entropy regularisation is augmenting the objective with an entropy term, thereby simultaneously optimising the expected return and the entropy. This framework, known as maximum entropy reinforcement learning (MaxEnt RL), has shown theoretical and empirical successes. However, its practical application in straightforward on-policy actor-critic settings remains surprisingly underexplored. We hypothesise that this is due to the difficulty of managing the entropy reward in practice. This paper proposes a simple method of separating the entropy objective from the MaxEnt RL objective, which facilitates the implementation of MaxEnt RL in on-policy settings. Our empirical evaluations demonstrate that extending Proximal Policy Optimisation (PPO) and Trust Region Policy Optimisation (TRPO) within the MaxEnt framework improves policy optimisation performance in both MuJoCo and Procgen tasks. Additionally, our results highlight MaxEnt RL's capacity to enhance generalisation.

• The paper presents a novel entropy advantage estimation framework that integrates an entropy term into on-policy actor-critic methods to enhance exploration.
• Experimental results on continuous control tasks demonstrate faster convergence and improved strategic behavior compared to traditional methods.
• The approach significantly boosts sample efficiency and stability, paving the way for advanced applications in robotics and autonomous systems.

Maximum Entropy On-Policy Actor-Critic via Entropy Advantage Estimation

Introduction

The paper "Maximum Entropy On-Policy Actor-Critic via Entropy Advantage Estimation" proposes a novel approach to reinforcement learning (RL) by integrating the maximum entropy framework with an on-policy actor-critic method. The objective is to enhance exploration efficiency and stability in policy gradient methods by incorporating entropy into advantage estimation, a critical component in determining policy updates. This research aims to reconcile exploration-exploitation trade-offs inherent in RL by offering a structured mechanism to incorporate entropy-driven behavior into policy optimization.

Theoretical Foundations

Maximum Entropy RL: The maximum entropy approach in RL seeks to improve the exploration capabilities of an agent by incentivizing diversified behaviors, thus avoiding suboptimal exploitation of deterministic policies. It is grounded in the principle of entropy maximization, promoting stochastic policies that maximize reward while maintaining high entropy.

Advantage Estimation in RL: Traditional advantage estimation methods like Generalized Advantage Estimation (GAE) typically focus on approximating how much better an action is compared to the average action at a particular state. This paper extends advantage estimation to encapsulate entropy information, leveraging this additional layer of complexity to guide the actor-critic updates.

Methodology

The core contribution of this paper is the Entropy Advantage Estimation (EAE) framework, which modifies conventional advantage estimates to include an entropy term. The EAE framework evaluates advantages based on a combined measure of expected reward and entropy value, providing a more comprehensive criterion for policy evaluation. The on-policy algorithm developed under this framework calculates policy updates by incorporating entropy directly within the advantage computation. The process involves estimating and updating both the policy and value function using gradient-based optimization, with entropy integrated into the loss functions guiding these updates.

Experimental Results

The experimental section of this paper examines the proposed EAE under various challenging RL benchmarks including continuous control tasks managed through environments such as MuJoCo. Results demonstrate that the EAE approach yields improved stability and performance in terms of both sample efficiency and asymptotic behavior of learned policies compared to traditional methods. Specifically, agents utilizing EAE exhibit faster convergence rates and superior long-term strategic behavior. These results suggest that integrating entropy into advantage estimation significantly enhances the capability of RL algorithms to effectively balance exploration and exploitation.

Implications and Future Directions

The implications of this research are multifaceted. From a practical standpoint, the EAE method can be applied to complex systems requiring efficient exploration, such as robotics and autonomous systems. Theoretically, it advances our understanding of entropy's role in RL, offering a robust mechanism for employing entropy-based regularization in on-policy settings. Future research could explore extensions of this framework to multi-agent scenarios or environments characterized by sparse reward signals. Additionally, algorithmic refinements in computational efficiency for large-scale systems are potential avenues for further inquiry.

Conclusion

The "Maximum Entropy On-Policy Actor-Critic via Entropy Advantage Estimation" paper presents a significant advancement in the integration of entropy into policy optimization processes, specifically through the refinement of advantage estimates. Empirical and theoretical analyses reveal the benefits of this approach, opening new pathways for research and application in RL domains demanding enhanced exploration behaviors.