Abstract
Exploration in multi-agent reinforcement learning is a challenging problem, especially in environments with sparse rewards. We propose a general method for efficient exploration by sharing experience amongst agents. Our proposed algorithm, called Shared Experience Actor-Critic (SEAC), applies experience sharing in an actor-critic framework. We evaluate SEAC in a collection of sparse-reward multi-agent environments and find that it consistently outperforms two baselines and two state-of-the-art algorithms by learning in fewer steps and converging to higher returns. In some harder environments, experience sharing makes the difference between learning to solve the task and not learning at all.
Original language | English |
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Title of host publication | Advances in Neural Information Processing Systems 33 (NeurIPS 2020) |
Publisher | Curran Associates Inc |
Pages | 10707-10717 |
Number of pages | 16 |
Publication status | Published - 6 Dec 2020 |
Event | Thirty-Fourth Conference on Neural Information Processing Systems - Virtual Conference Duration: 6 Dec 2020 → 12 Dec 2020 https://nips.cc/Conferences/2020 |
Publication series
Name | |
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ISSN (Print) | 1049-5258 |
Conference
Conference | Thirty-Fourth Conference on Neural Information Processing Systems |
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Abbreviated title | NeurIPS 2020 |
City | Virtual Conference |
Period | 6/12/20 → 12/12/20 |
Internet address |