Abstract:
In this paper, we train reinforcement learning agents on the game of DeepRTS under different training strategies, which are i) training against rule based agents, ii) sel...Show MoreMetadata
Abstract:
In this paper, we train reinforcement learning agents on the game of DeepRTS under different training strategies, which are i) training against rule based agents, ii) self-training and iii) training by adversarial attack to another agent. We perform certain modifications on the DeepRTS game and the reinforcement learning framework to make it closer to real life decision making problems. For this purpose, we allow agents take macro actions based on human heuristics, where these actions may last multiple time steps and the durations for these actions may differ from each other. In addition, the agents simultaneously take actions for each available unit at a time step. We train the reinforcement learning based agents under three different training strategies and we provide a detailed performance analysis of these agents against several reference agents.
Date of Conference: 15-18 May 2022
Date Added to IEEE Xplore: 29 August 2022
ISBN Information:
Print on Demand(PoD) ISSN: 2165-0608