Proximal Policy Optimization with Adaptive Threshold for Symmetric Relative Density Ratio
Deep reinforcement learning (DRL) is one of the promising approaches for introducing robots into complicated environments. The recent remarkable progress of DRL stands on regularization of policy, which allows the policy to improve stably and efficiently. A popular method, so-called proximal policy optimization (PPO), and its variants constrain density ratio of the latest and baseline policies when the density ratio exceeds a given threshold. This threshold can be designed relatively intuitively, and in fact its recommended value range has been suggested. However, the density ratio is asymmetric for its center, and the possible error scale from its center, which should be close to the threshold, would depend on how the baseline policy is given. In order to maximize the values of regularization of policy, this paper proposes a new PPO derived using relative Pearson (RPE) divergence, therefore so-called PPO-RPE, to design the threshold adaptively. In PPO-RPE, the relative density ratio, which can be formed with symmetry, replaces the raw density ratio. Thanks to this symmetry, its error scale from center can easily be estimated, hence, the threshold can be adapted for the estimated error scale. From three simple benchmark simulations, the importance of algorithm-dependent threshold design is revealed. By simulating additional four locomotion tasks, it is verified that the proposed method statistically contributes to task accomplishment by appropriately restricting the policy updates.
Code (0)
등록된 구현이 없습니다.
Tasks
Deep Reinforcement LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Proximal Policy Optimization with Relative Pearson Divergence
The recent remarkable progress of deep reinforcement learning (DRL) stands on regularization of policy for stable and efficient learning. A popular method, named proximal policy optimization (PPO), has been introduced fo…
Deep Reinforcement LearningFlow-DPPO: Divergence Proximal Policy Optimization for Flow Matching Models
Recent work has demonstrated that online reinforcement learning (RL) can substantially improve the quality and alignment of flow matching models for image and video generation. Methods such as Flow-GRPO and CPS cast the …
Reinforcement LearningVideo GenerationDistributed Proximal Policy Optimization for Contention-Based Spectrum Access
The increasing number of wireless devices operating in unlicensed spectrum motivates the development of intelligent adaptive approaches to spectrum access that go beyond traditional carrier sensing. We develop a novel di…
FairnessSAFE: Stable Alignment Finetuning with Entropy-Aware Predictive Control for Reinforcement Learning from Human Feedback (RLHF)
Proximal Policy Optimization (PPO) has been positioned by recent literature as the canonical method for the RL part of Reinforcement Learning from Human Feedback (RLHF). PPO performs well empirically but has a heuristic …
Reinforcement LearningProximal Policy Optimization with Adaptive Exploration
Proximal Policy Optimization with Adaptive Exploration (axPPO) is introduced as a novel learning algorithm. This paper investigates the exploration-exploitation tradeoff within the context of reinforcement learning and a…
reinforcement-learningReinforcement Learning