2023-03-17 更新
Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement Learning
Authors:Xutong Zhao, Yangchen Pan, Chenjun Xiao, Sarath Chandar, Janarthanan Rajendran
Efficient exploration is critical in cooperative deep Multi-Agent Reinforcement Learning (MARL). In this paper, we propose an exploration method that efficiently encourages cooperative exploration based on the idea of the theoretically justified tree search algorithm UCT (Upper Confidence bounds applied to Trees). The high-level intuition is that to perform optimism-based exploration, agents would achieve cooperative strategies if each agent’s optimism estimate captures a structured dependency relationship with other agents. At each node (i.e., action) of the search tree, UCT performs optimism-based exploration using a bonus derived by conditioning on the visitation count of its parent node. We provide a perspective to view MARL as tree search iterations and develop a method called Conditionally Optimistic Exploration (COE). We assume agents take actions following a sequential order, and consider nodes at the same depth of the search tree as actions of one individual agent. COE computes each agent’s state-action value estimate with an optimistic bonus derived from the visitation count of the state and joint actions taken by agents up to the current agent. COE is adaptable to any value decomposition method for centralized training with decentralized execution. Experiments across various cooperative MARL benchmarks show that COE outperforms current state-of-the-art exploration methods on hard-exploration tasks.
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SVDE: Scalable Value-Decomposition Exploration for Cooperative Multi-Agent Reinforcement Learning
Authors:Shuhan Qi, Shuhao Zhang, Qiang Wang, Jiajia Zhang, Jing Xiao, Xuan Wang
Value-decomposition methods, which reduce the difficulty of a multi-agent system by decomposing the joint state-action space into local observation-action spaces, have become popular in cooperative multi-agent reinforcement learning (MARL). However, value-decomposition methods still have the problems of tremendous sample consumption for training and lack of active exploration. In this paper, we propose a scalable value-decomposition exploration (SVDE) method, which includes a scalable training mechanism, intrinsic reward design, and explorative experience replay. The scalable training mechanism asynchronously decouples strategy learning with environmental interaction, so as to accelerate sample generation in a MapReduce manner. For the problem of lack of exploration, an intrinsic reward design and explorative experience replay are proposed, so as to enhance exploration to produce diverse samples and filter non-novel samples, respectively. Empirically, our method achieves the best performance on almost all maps compared to other popular algorithms in a set of StarCraft II micromanagement games. A data-efficiency experiment also shows the acceleration of SVDE for sample collection and policy convergence, and we demonstrate the effectiveness of factors in SVDE through a set of ablation experiments.
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