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Open-Ended Learning Leads to Generally Capable Agents
In this work we create agents that can perform well beyond a single, ind...
07/27/2021 ∙ by Open-Ended Learning Team, et al. ∙
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SoftDICE for Imitation Learning: Rethinking Off-policy Distribution Matching
We present SoftDICE, which achieves state-of-the-art performance for imi...
06/06/2021 ∙ by Mingfei Sun, et al. ∙
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Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning
Reinforcement Learning in large action spaces is a challenging problem. ...
05/31/2021 ∙ by Anuj Mahajan, et al. ∙
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UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning
This paper focuses on cooperative value-based multi-agent reinforcement ...
10/06/2020 ∙ by Tarun Gupta, et al. ∙
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MAVEN: Multi-Agent Variational Exploration
Centralised training with decentralised execution is an important settin...
10/16/2019 ∙ by Anuj Mahajan, et al. ∙
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VIREL: A Variational Inference Framework for Reinforcement Learning
Applying probabilistic models to reinforcement learning (RL) has become ...
11/03/2018 ∙ by Matthew Fellows, et al. ∙
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Symmetry Learning for Function Approximation in Reinforcement Learning
In this paper we explore methods to exploit symmetries for ensuring samp...
06/09/2017 ∙ by Anuj Mahajan, et al. ∙
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