400 citations · 506 across the 9 of their papers we have counts for
8 papers · 1 filter
Cleanba: A Reproducible and Efficient Distributed Reinforcement Learning Platform
Shengyi Huang, Jiayi Weng, Rujikorn Charakorn +3
Distributed Deep Reinforcement Learning (DRL) aims to leverage more computational resources to train autonomous agents with less training time. Despite recent progress in the field…
Implicit Stochastic Gradient Descent for Training Physics-informed Neural Networks
Ye Li, Song-Can Chen, Sheng-Jun Huang
Physics-informed neural networks (PINNs) have effectively been demonstrated in solving forward and inverse differential equation problems, but they are still trapped in training fa…
EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine
Jiayi Weng, Min Lin, Shengyi Huang +9
There has been significant progress in developing reinforcement learning (RL) training systems. Past works such as IMPALA, Apex, Seed RL, Sample Factory, and others, aim to improve…
A2C is a special case of PPO
Shengyi Huang, Anssi Kanervisto, Antonin Raffin +3
Advantage Actor-critic (A2C) and Proximal Policy Optimization (PPO) are popular deep reinforcement learning algorithms used for game AI in recent years. A common understanding is t…
CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms
Shengyi Huang, Rousslan Fernand Julien Dossa, Chang Ye +1
CleanRL is an open-source library that provides high-quality single-file implementations of Deep Reinforcement Learning algorithms. It provides a simpler yet scalable developing ex…
Action Guidance: Getting the Best of Sparse Rewards and Shaped Rewards for Real-time Strategy Games
Shengyi Huang, Santiago Ontañón
Training agents using Reinforcement Learning in games with sparse rewards is a challenging problem, since large amounts of exploration are required to retrieve even the first rewar…
A Closer Look at Invalid Action Masking in Policy Gradient Algorithms
Shengyi Huang, Santiago Ontañón
In recent years, Deep Reinforcement Learning (DRL) algorithms have achieved state-of-the-art performance in many challenging strategy games. Because these games have complicated ru…
Comparing Observation and Action Representations for Deep Reinforcement Learning in RTS
Shengyi Huang, Santiago Ontañón
This paper presents a preliminary study comparing different observation and action space representations for Deep Reinforcement Learning (DRL) in the context of Real-time Strategy…