Proximal Policy Optimization

Proximal Policy Optimization (PPO) is a reinforcement learning algorithm used to train agents to make optimal decisions in complex environments, with a current research focus on improving its efficiency and robustness. Recent work explores enhancements such as refined credit assignment methods (e.g., VinePPO), incorporation of human feedback and safety mechanisms (e.g., HI-PPO, PRPO), and addressing challenges in high-dimensional spaces and sample efficiency through techniques like diffusion model integration. These advancements are significant for various applications, including robotics, autonomous systems, and large language model alignment, where PPO's ability to learn effective policies from interactions with the environment is crucial.

Papers

May 4, 2023

Simple Noisy Environment Augmentation for Reinforcement Learning
Raad Khraishi, Ramin Okhrati
Reinforcement Learning Data Augmentation Proximal Policy Optimization Augmentation Technique Image Augmentation Augmented Sample

April 26, 2023

Can Agents Run Relay Race with Strangers? Generalization of RL to Out-of-Distribution Trajectories
Li-Cheng Lan, Huan Zhang, Cho-Jui Hsieh
Reinforcement Learning Strong Generalization Agent Smith Proximal Policy Optimization Agent Performance Structural Generalization RELAY Selection

April 4, 2023

Regularization of the policy updates for stabilizing Mean Field Games
Talal Algumaei, Ruben Solozabal, Reda Alami, Hakim Hacid, Merouane Debbah, Martin Takac
Deep Reinforcement Learning Proximal Policy Optimization Mean Field Cooperative Multi Agent Reinforcement Learning Mean Field Game Policy Update

March 31, 2023

Understanding Reinforcement Learning Algorithms: The Progress from Basic Q-learning to Proximal Policy Optimization
Mohamed-Amine Chadi, Hajar Mousannif
Reinforcement Learning Artificial Intelligence Q Learning Proximal Policy Optimization Reinforcement Learning Algorithm Much Progress State of the Art Algorithm

March 23, 2023

RLOR: A Flexible Framework of Deep Reinforcement Learning for Operation Research
Ching Pui Wan, Tung Li, Jason Min Wang
Reinforcement Learning Deep Reinforcement Learning Proximal Policy Optimization Flexible Framework Operation Research

March 22, 2023

March 15, 2023

March 7, 2023

A Multiplicative Value Function for Safe and Efficient Reinforcement Learning
Nick Bührer, Zhejun Zhang, Alexander Liniger, Fisher Yu, Luc Van Gool
Proximal Policy Optimization Safe Reinforcement Learning Value Function Efficient Reinforcement Learning Reward Learning

February 26, 2023

Reinforcement Learning Based Pushing and Grasping Objects from Ungraspable Poses
Hao Zhang, Hongzhuo Liang, Lin Cong, Jianzhi Lyu, Long Zeng, Pingfa Feng, Jianwei Zhang
Proximal Policy Optimization Variational Method Functional Grasping Model Free Deep Reinforcement Learning Grasping Object Stable Pushing Unusual Pose

February 22, 2023

Behavior Proximal Policy Optimization
Zifeng Zhuang, Kun Lei, Jinxin Liu, Donglin Wang, Yilang Guo
Offline Reinforcement Learning Proximal Policy Optimization Offline Policy Policy Actor Critic

February 17, 2023

Deep Reinforcement Learning for mmWave Initial Beam Alignment
Daniel Tandler, Sebastian Dörner, Marc Gauger, Stephan ten Brink
Deep Reinforcement Learning Proximal Policy Optimization Deep Reinforcement Beam Management

February 1, 2023

Bridging Physics-Informed Neural Networks with Reinforcement Learning: Hamilton-Jacobi-Bellman Proximal Policy Optimization (HJBPPO)
Amartya Mukherjee, Jun Liu
Reinforcement Learning Neural Network Proximal Policy Optimization Bridging Text Hamilton Jacobi Bellman

January 26, 2023

January 10, 2023

Imbalanced Classification In Faulty Turbine Data: New Proximal Policy Optimization
Mohammad Hossein Modirrousta, Mahdi Aliyari Shoorehdeli, Mostafa Yari, Arash Ghahremani
Reinforcement Learning Proximal Policy Optimization Fault Detection Imbalanced Classification Data Driven Fault Limited Fault

January 9, 2023

Transformers as Policies for Variable Action Environments
Niklas Zwingenberger
Transformer Megatron Decepticons Pre Trained Model Proximal Policy Optimization Transformer Encoder Prior Policy

December 23, 2022

Proximal Policy Optimization with Graph Neural Networks for Optimal Power Flow
Ángela López-Cardona, Guillermo Bernárdez, Pere Barlet-Ros, Albert Cabellos-Aparicio
Graph Neural Network Proximal Policy Optimization Power Grid Optimal Power Flow Power Network

December 13, 2022

Proximal Policy Optimization Based Reinforcement Learning for Joint Bidding in Energy and Frequency Regulation Markets
Muhammad Anwar, Changlong Wang, Frits de Nijs, Hao Wang
Proximal Policy Optimization Energy Policy Research Electricity Market Energy Market Optimal Bidding Strategy Frequency Control Energy Arbitrage

Proximal Policy Optimization

Papers

Simple Noisy Environment Augmentation for Reinforcement Learning

Can Agents Run Relay Race with Strangers? Generalization of RL to Out-of-Distribution Trajectories

Regularization of the policy updates for stabilizing Mean Field Games

Understanding Reinforcement Learning Algorithms: The Progress from Basic Q-learning to Proximal Policy Optimization

RLOR: A Flexible Framework of Deep Reinforcement Learning for Operation Research

$P^{3}O$: Transferring Visual Representations for Reinforcement Learning via Prompting

A Hierarchical Hybrid Learning Framework for Multi-agent Trajectory Prediction

Efficient Planning of Multi-Robot Collective Transport using Graph Reinforcement Learning with Higher Order Topological Abstraction

Muti-Agent Proximal Policy Optimization For Data Freshness in UAV-assisted Networks

A Multiplicative Value Function for Safe and Efficient Reinforcement Learning

Reinforcement Learning Based Pushing and Grasping Objects from Ungraspable Poses

Behavior Proximal Policy Optimization

Deep Reinforcement Learning for mmWave Initial Beam Alignment

Bridging Physics-Informed Neural Networks with Reinforcement Learning: Hamilton-Jacobi-Bellman Proximal Policy Optimization (HJBPPO)

Partial advantage estimator for proximal policy optimization

Joint action loss for proximal policy optimization

Imbalanced Classification In Faulty Turbine Data: New Proximal Policy Optimization

Transformers as Policies for Variable Action Environments

Proximal Policy Optimization with Graph Neural Networks for Optimal Power Flow

Proximal Policy Optimization Based Reinforcement Learning for Joint Bidding in Energy and Frequency Regulation Markets