Abstract
Curriculum reinforcement learning (CRL) improves the learning speed and stability of an agent by exposing it to a tailored series of tasks throughout learning. Despite empirical successes, an open question in CRL is how to automatically generate a curriculum for a given reinforcement learning (RL) agent, avoiding manual design. In this paper, we propose an answer by interpreting the curriculum generation as an inference problem, where distributions over tasks are progressively learned to approach the target task. This approach leads to an automatic curriculum generation, whose pace is controlled by the agent, with solid theoretical motivation and easily integrated with deep RL algorithms. In the conducted experiments, the curricula generated with the proposed algorithm significantly improve learning performance across several environments and deep RL algorithms, matching or outperforming state-of-the-art existing CRL algorithms.
Original language | English |
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Title of host publication | Proceedings of the 34th Conference on Neural Information Processing Systems, NeurIPS 2020 |
Number of pages | 12 |
Publication status | E-pub ahead of print - 2020 |
MoE publication type | A4 Article in a conference publication |
Event | Conference on Neural Information Processing Systems - Virtual, Vancouver, Canada Duration: 6 Dec 2020 → 12 Dec 2020 Conference number: 34 |
Publication series
Name | Advances in neural information processing systems |
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Publisher | Morgan Kaufmann Publishers |
Volume | 33 |
ISSN (Print) | 1049-5258 |
Conference
Conference | Conference on Neural Information Processing Systems |
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Abbreviated title | NeurIPS |
Country | Canada |
City | Vancouver |
Period | 06/12/2020 → 12/12/2020 |