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Relative Entropy Policy Search
Open accessAbstract
Policy search is a successful approach to reinforcement learning. However, policy improvements often result in the loss of information. Hence, it has been marred by premature convergence and implausible solutions. As first suggested in the context of covariant policy gradients, many of these problems may be addressed by constraining the information loss. In this paper, we continue this path of reasoning and suggest the Relative Entropy Policy Search (REPS) method. The resulting method differs significantly from previous policy gradient approaches and yields an exact update step. It can be shown to work well on typical reinforcement learning benchmark problems.
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Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
Markov decision processes: Discrete stochastic dynamic programming
Hazeghi, K., & Puterman, M. L. (1995) · Journal of the American Statistical Association · doi:10.2307/2291177 · Cited by 8,037
Cited by
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Trust region policy optimization
Schulman, J., Levine, S., Moritz, P., Jordan, M. I., & Abbeel, P. (2015) · arXiv · doi:10.48550/arxiv.1502.05477 · Cited by 4,578
Reinforcement learning in robotics: A survey
Kober, J., Bagnell, J. A., & Peters, J. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913495721 · Cited by 3,160
Self-citationBenchmarking Deep Reinforcement Learning for Continuous Control
Duan, Y., Chen, X., Houthooft, R., Schulman, J., & Abbeel, P. (2016) · arXiv · doi:10.48550/arxiv.1604.06778 · Cited by 1,114
Overcoming Exploration in Reinforcement Learning with Demonstrations
Nair, A., McGrew, B., Andrychowicz, M., Zaremba, W., & Abbeel, P. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2018.8463162 · Cited by 712
Metadata
- Authors
- Peters, J., Mulling, K., & Altun, Y.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2010-07-05
- Volume
- 24
- Issue
- 1
- Pages
- 1607-1612
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W1499669280
- Cited by
- 455
- References
- 2
- FWCI
- 7.02
- Type
- conference paper
- Language
- en
- Open access
- diamond
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- Peters, J., Mulling, K., & Altun, Y. (2010). Relative Entropy Policy Search. Proceedings of the AAAI Conference on Artificial Intelligence. https://doi.org/10.1609/aaai.v24i1.7727
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