ikostrikov/pytorch-trpo
A PyTorch implementation of Trust Region Policy Optimization, a deep reinforcement learning algorithm for continuous robotic control tasks.
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This repository provides a PyTorch implementation of TRPO (Trust Region Policy Optimization), a policy gradient method for training reinforcement learning agents in continuous control environments. The implementation uses exact Hessian-vector products for computing natural gradient updates, offering better precision than finite differences approximations. It is designed to work with Mujoco physics simulation environments for training robotic control policies, with configurable hyperparameters for tasks like Reacher, Hopper, Walker2d, and Humanoid.
Frequently asked
- What is ikostrikov/pytorch-trpo?
- A PyTorch implementation of Trust Region Policy Optimization, a deep reinforcement learning algorithm for continuous robotic control tasks.
- Is pytorch-trpo open source?
- Yes — ikostrikov/pytorch-trpo is open source, released under the MIT license.
- What language is pytorch-trpo written in?
- ikostrikov/pytorch-trpo is primarily written in Python.
- How popular is pytorch-trpo?
- ikostrikov/pytorch-trpo has 448 stars on GitHub.
- Where can I find pytorch-trpo?
- ikostrikov/pytorch-trpo is on GitHub at https://github.com/ikostrikov/pytorch-trpo.