A Reinforcement Learning based End-to-End Algorithm for Confrontation Problem
| Autor: | Siqiang Wang, Haodi Yao, He Fenghua, Yu Yao |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
020901 industrial engineering & automation End-to-end principle Computer science 0202 electrical engineering electronic engineering information engineering Task analysis Reinforcement learning 020201 artificial intelligence & image processing 02 engineering and technology ComputingMethodologies_ARTIFICIALINTELLIGENCE Algorithm |
| Zdroj: | 2019 Chinese Control Conference (CCC). |
| Popis: | In this paper, a confrontation problem between two agents is investigated in which one agent is required to find then eliminate the other agent through projecting bullets. A reinforcement learning based algorithm is designed to realize an end-to-end strategy for one agent to confront the other. Self-play method is used to enhance the algorithm. Simulation results show the effectiveness of the proposed algorithm in confrontation with FSM based strategy and human player. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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