arXiv:2103.00187
공식 코드 · Papers with Code
Multi-agent Reinforcement Learning in OpenSpiel: A Reproduction Report
원문 초록
In this report, we present results reproductions for several core algorithms implemented in the OpenSpiel framework for learning in games. The primary contribution of this work is a validation of OpenSpiel's re-implemented search and Reinforcement Learning algorithms against the results reported in their respective originating works. Additionally, we provide complete documentation of hyperparameters and source code required to reproduce these experiments easily and exactly.
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Multi-Agent Reinforcement Learning for Autonomous Multi-Satellite Earth Observation: A Realistic Case Study
Decentralizing Multi-Agent Reinforcement Learning with Temporal Causal Information
Robust Multi-Agent Reinforcement Learning with Model Uncertainty
Multi-Step Reinforcement Learning for Single Image Super-Resolution
Multirobot Coverage of Modular Environments
High-Throughput Distributed Reinforcement Learning via Adaptive Policy Synchronization
토론
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