Papers › Trust Region Policy Optimization
Trust Region Policy Optimization
John Schulman, Sergey Levine, Philipp Moritz, Michael. I. Jordan, Pieter Abbeel
We describe an iterative procedure for optimizing policies, with guaranteed monotonic improvement. By making several approximations to the theoretically-justified procedure, we develop a practical algorithm, called Trust Region Policy Optimization (TRPO). This algorithm is similar to natural policy gradient methods and 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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Code
Syntology Ran 7 of 17 code samples harvested from 7 repositories linked to this paper; 10 have no recorded run. Of those that ran: 2 ran · honoured contract; 1 ran · fixture could not drive it; 4 ran with no contract checked.
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Code Syntology ran Syntology
17 samples harvested; 7 ran; 2 honoured the contract we drafted; 10 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
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Methods
Introduced by this paper: TRPO
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