Optimistic Reinforcement Learning by Forward Kullback-Leibler Divergence Optimization

Abstract

This paper addresses a new interpretation of reinforcement learning (RL) as reverse Kullback-Leibler (KL) divergence optimization, and derives a new optimization method using forward KL divergence. Although RL originally aims to maximize return indirectly through optimization of policy, the recent work by Levine has proposed a different derivation process with explicit consideration of optimality as stochastic variable. This paper follows this concept and formulates the traditional learning laws for both value function and policy as the optimization problems with reverse KL divergence including optimality. Focusing on the asymmetry of KL divergence, the new optimization problems with forward KL divergence are derived. Remarkably, such new optimization problems can be regarded as optimistic RL. That optimism is intuitively specified by a hyperparameter converted from an uncertainty parameter. In addition, it can be enhanced when it is integrated with prioritized experience replay and eligibility traces, both of which accelerate learning. The effects of this expected optimism was investigated through learning tendencies on numerical simulations using Pybullet. As a result, moderate optimism accelerated learning and yielded higher rewards. In a realistic robotic simulation, the proposed method with the moderate optimism outperformed one of the state-of-the-art RL method.

Consolidated Adaptive T-soft Update for Deep Reinforcement Learning

“…position) are unobserved, making this task a partially observed MDP (POMDP). Note that the default setting for Minitaur tasks is unrealistic, as pointed out in the literature [25]. Therefore, it was modified as shown in Table III (arguments not listed are left at default).…”