3 papers
cs.LG2022
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…
cs.LG2020
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…
cs.LG2019
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…