Is Vanilla Policy Gradient Overlooked? Analyzing Deep Reinforcement Learning for Hanabi
Autor: | Grooten, Bram, Wemmenhove, Jelle, Poot, Maurice, Portegies, Jim |
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Rok vydání: | 2022 |
Předmět: | |
Druh dokumentu: | Working Paper |
Popis: | In pursuit of enhanced multi-agent collaboration, we analyze several on-policy deep reinforcement learning algorithms in the recently published Hanabi benchmark. Our research suggests a perhaps counter-intuitive finding, where Proximal Policy Optimization (PPO) is outperformed by Vanilla Policy Gradient over multiple random seeds in a simplified environment of the multi-agent cooperative card game. In our analysis of this behavior we look into Hanabi-specific metrics and hypothesize a reason for PPO's plateau. In addition, we provide proofs for the maximum length of a perfect game (71 turns) and any game (89 turns). Our code can be found at: https://github.com/bramgrooten/DeepRL-for-Hanabi Comment: Accepted at ALA 2022 (Adaptive and Learning Agents Workshop at AAMAS 2022) |
Databáze: | arXiv |
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