Maximum Entropy On-Policy Actor-Critic via Entropy Advantage Estimation (2407.18143v1)

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 introduces entropy advantage estimation to enhance the on-policy actor-critic framework in maximum entropy reinforcement learning.
• The methodology balances exploration and exploitation by integrating a crucial entropy regularization term into the policy optimization objective.
• Experimental results across benchmarks demonstrate improved policy stability, learning efficiency, and convergence compared to traditional models.

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

The paper "Maximum Entropy On-Policy Actor-Critic via Entropy Advantage Estimation" by Jean Seong Bjorn Choe and Jong-Kook Kim presents an innovative method in the domain of maximum entropy reinforcement learning (MaxEnt RL). This research primarily focuses on enhancing the actor-critic framework through an approach known as entropy advantage estimation.

The methodology proposed in this paper undertakes the augmentation of standard on-policy actor-critic algorithms by incorporating entropy advantage estimation. The aim is to leverage the benefits of the maximum entropy framework, which is known for encouraging exploration by adding an entropy regularization term to the policy optimization objective. This approach not only improves the robustness of the policy but also addresses the balance between exploration and exploitation effectively.

One of the critical aspects of this paper is the formulation of the entropy advantage, a concept that quantifies the improvement in policy performance by integrating entropy measures. The entropy advantage is estimated to inform the policy optimization process under the MaxEnt framework. This work stands on a firm theoretical foundation, showing that the entropy advantage can provide a reliable guideline for updating the policy, resulting in better convergence properties and improved overall performance.

The experimental section of the paper offers strong numerical support for the proposed framework. The authors conduct a series of experiments across various benchmark environments, demonstrating substantial performance improvements over baseline actor-critic models. Specifically, the results exhibit superior stability and efficiency in learning policies, underscoring the practical implications of entropy-augmented techniques in reinforcement learning.

The strong claims put forward by this paper suggest that using entropy advantage estimation can bridge gaps associated with conventional policy gradient methods, particularly in settings where exploration-exploitation trade-offs are critical. While the paper does not suggest a one-size-fits-all remedy, it implies that such enhancement can be systematically beneficial, especially in complex environments.

From a theoretical perspective, this approach offers insights into the role of entropy in reinforcement learning. By presenting a mathematically robust modification to the actor-critic paradigm, it paves the way for future research into integrating entropy-driven mechanisms within other RL frameworks. The implications of these findings are likely to stimulate further exploration into the domains of hierarchical and multi-agent reinforcement learning, where robustness and adaptability are paramount.

Overall, this paper contributes extensively to the advancement of reinforcement learning methodologies, particularly by extending the applicability and efficiency of MaxEnt RL paradigms. Future developments could involve extending this framework to off-policy contexts or exploring its interaction with other regularization techniques to further optimize learning outcomes.