Scalable Deep Multi-Agent Reinforcement Learning via Observation Embedding and Parameter Noise

In this paper, we explore a scalable deep reinforcement learning (DRL) method for environments with multi-agents. Due to the explosive increase of the input dimensionality with the number of agents, most existing DRL methods are only able to cope with single-agent settings, or for only a small number of agents. To address this problem, we adopt a centralized training with decentralized execution framework, in which an observation embedding is used to reduce the curse of dimensionality. Perturbations are injected into the parameter space of the actor network for each agent to encourage exploration. The experiments demonstrate the effectiveness of the proposed approach on both cooperative and competitive environments.