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In pursuit of enhanced multi-agent collaboration, we analyze severalon-policy deep reinforcement learning algorithms in the recently publishedHanabi benchmark. Our research suggests a perhaps counter-intuitive finding,where Proximal Policy Optimization (PPO) is outperformed by Vanilla PolicyGradient over multiple random seeds in a simplified environment of themulti-agent cooperative card game. In our analysis of this behavior we lookinto Hanabi-specific metrics and hypothesize a reason for PPO's plateau. Inaddition, 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

Is Vanilla Policy Gradient Overlooked? Analyzing Deep Reinforcement Learning for Hanabi