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Multi-Agent Deep Reinforcement Learning with Adaptive Policies
[article]
2019
arXiv
pre-print
We propose a novel approach to address one aspect of the non-stationarity problem in multi-agent reinforcement learning (RL), where the other agents may alter their policies due to environment changes during execution. This violates the Markov assumption that governs most single-agent RL methods and is one of the key challenges in multi-agent RL. To tackle this, we propose to train multiple policies for each agent and postpone the selection of the best policy at execution time. Specifically, we
arXiv:1912.00949v1
fatcat:tivvmotqwffyfkowmcxid6hjcu