The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Multi-Task Deep Reinforcement Learning with PopArt
Open accessAbstract
The reinforcement learning (RL) community has made great strides in designing algorithms capable of exceeding human performance on specific tasks. These algorithms are mostly trained one task at the time, each new task requiring to train a brand new agent instance. This means the learning algorithm is general, but each solution is not; each agent can only solve the one task it was trained on. In this work, we study the problem of learning to master not one but multiple sequentialdecision tasks at once. A general issue in multi-task learning is that a balance must be found between the needs of multiple tasks competing for the limited resources of a single learning system. Many learning algorithms can get distracted by certain tasks in the set of tasks to solve. Such tasks appear more salient to the learning process, for instance because of the density or magnitude of the in-task rewards. This causes the algorithm to focus on those salient tasks at the expense of generality. We propose to automatically adapt the contribution of each task to the agent’s updates, so that all tasks have a similar impact on the learning dynamics. This resulted in state of the art performance on learning to play all games in a set of 57 diverse Atari games. Excitingly, our method learned a single trained policy - with a single set of weights - that exceeds median human performance. To our knowledge, this was the first time a single agent surpassed human-level performance on this multi-task domain. The same approach also demonstrated state of the art performance on a set of 30 tasks in the 3D reinforcement learning platform DeepMind Lab.
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Self-citationGradient Surgery for Multi-Task Learning
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Multi-Task Learning with Deep Neural Networks: A Survey
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Towards Continual Reinforcement Learning: A Review and Perspectives
Khetarpal, K., Riemer, M., Rish, I., & Precup, D. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13673 · Cited by 224
Metadata
- Authors
- Hessel, M., Soyer, H., Espeholt, L., Czarnecki, W., Schmitt, S., & Van Hasselt, H.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2019-07-17
- Volume
- 33
- Issue
- 01
- Pages
- 3796-3803
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2891076394
- Cited by
- 196
- References
- 15
- FWCI
- 12.42
- Type
- conference paper
- Language
- en
- Open access
- diamond
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- Hessel, M., Soyer, H., Espeholt, L., Czarnecki, W., Schmitt, S., & Van Hasselt, H. (2019). Multi-Task Deep Reinforcement Learning with PopArt. Proceedings of the AAAI Conference on Artificial Intelligence. https://doi.org/10.1609/aaai.v33i01.33013796
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