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Article

Trust region policy optimization

Authors:

Philipp Moritz,

Michael Jordan,

Pieter AbbeelAuthors Info & Claims

ICML'15: Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37

Pages 1889 - 1897

Published: 06 July 2015 Publication History

Abstract

In this article, we describe a method for optimizing control policies, with guaranteed monotonic improvement. By making several approximations to the theoretically-justified scheme, we develop a practical algorithm, called Trust Region Policy Optimization (TRPO). This algorithm is effective for optimizing large nonlinear policies such as neural networks. Our experiments demonstrate its robust performance on a wide variety of tasks: learning simulated robotic swimming, hopping, and walking gaits; and playing Atari games using images of the screen as input. Despite its approximations that deviate from the theory, TRPO tends to give monotonic improvement, with little tuning of hyperparameters.

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Cited By

Jordan PGrötschla FFan FWattenhofer RDastani MSichman JAlechina NDignum V(2024)Decentralized Federated Policy Gradient with Byzantine Fault-Tolerance and Provably Fast ConvergenceProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3662951(964-972)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.5555/3635637.3662951
Chen WOnyejizu JVu LHoang LSubramanian DKar KMishra SPaternain SDastani MSichman JAlechina NDignum V(2024)Adaptive Primal-Dual Method for Safe Reinforcement LearningProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3662881(326-334)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.5555/3635637.3662881
Li YQi SWang XZhang JCui L(2024)A Novel Tree-Based Method for Interpretable Reinforcement LearningACM Transactions on Knowledge Discovery from Data10.1145/369546418:9(1-22)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3695464
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Trust region policy optimization
1. Computing methodologies
2. Theory of computation
  1. Design and analysis of algorithms

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cover image Guide Proceedings

ICML'15: Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37

July 2015

2558 pages

Editors:
Francis Bach,
David Blei

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JMLR.org

Publication History

Published: 06 July 2015

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Cited By

Jordan PGrötschla FFan FWattenhofer RDastani MSichman JAlechina NDignum V(2024)Decentralized Federated Policy Gradient with Byzantine Fault-Tolerance and Provably Fast ConvergenceProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3662951(964-972)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.5555/3635637.3662951
Chen WOnyejizu JVu LHoang LSubramanian DKar KMishra SPaternain SDastani MSichman JAlechina NDignum V(2024)Adaptive Primal-Dual Method for Safe Reinforcement LearningProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3662881(326-334)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.5555/3635637.3662881
Li YQi SWang XZhang JCui L(2024)A Novel Tree-Based Method for Interpretable Reinforcement LearningACM Transactions on Knowledge Discovery from Data10.1145/369546418:9(1-22)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3695464
Ding XDu W(2024)Optimizing Irrigation Efficiency using Deep Reinforcement Learning in the FieldACM Transactions on Sensor Networks10.1145/366218220:4(1-34)Online publication date: 8-Jul-2024
https://dl.acm.org/doi/10.1145/3662182
Mo KYe PRen XWang SLi WLi J(2024)Security and Privacy Issues in Deep Reinforcement Learning: Threats and CountermeasuresACM Computing Surveys10.1145/364031256:6(1-39)Online publication date: 12-Jan-2024
https://dl.acm.org/doi/10.1145/3640312
Le AChalvatzaki GBiess APeters JOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Accelerating motion planning via optimal transportProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3669552(78453-78482)Online publication date: 10-Dec-2023
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Tiapkin DBelomestny DCalandriello DMoulines ÉMunos RNaumov APerrault PValko MMénard POh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Model-free posterior sampling via learning rate randomizationProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3669347(73719-73774)Online publication date: 10-Dec-2023
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Zhang XChen JWang HXie HLiu YLui JLi HOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Uncertainty-aware instance reweighting for off-policy learningProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3669346(73691-73718)Online publication date: 10-Dec-2023
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Morad SKortvelesy RLiwicki SProrok AOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Reinforcement learning with fast and forgetful memoryProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3669272(72008-72029)Online publication date: 10-Dec-2023
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Ganai MGong ZYu CHerbert SGao SOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Iterative reachability estimation for safe reinforcement learningProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3669180(69764-69797)Online publication date: 10-Dec-2023
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