pbo
nes-torch
| pbo | nes-torch | |
|---|---|---|
| 1 | 3 | |
| 14 | 17 | |
| - | - | |
| 3.0 | 3.6 | |
| 8 months ago | over 2 years ago | |
| Python | Python | |
| MIT License | MIT License |
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pbo
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Why we use diagonal gaussian rather than multivariate guassian (with full covariance matrix)
I have implemented full covariance matrix output from neural networks in a drl-related project, in which I devised an optimization technique out of mixing a PG approach with CMA-ES concepts, and you can find a possible implementation of how to do so here: https://github.com/jviquerat/pbo In this specific context, full covariance matrix gave a very significant performance boost. Yet I don't want to draw premature conclusions on whether this will end up the same when plugged into PPO.
nes-torch
What are some alternatives?
pipcs - PIPCS is Python Configuration System
pytorch-learn-reinforcement-learning - A collection of various RL algorithms like policy gradients, DQN and PPO. The goal of this repo will be to make it a go-to resource for learning about RL. How to visualize, debug and solve RL problems. I've additionally included playground.py for learning more about OpenAI gym, etc.
simple-es - Simple implementations of multi-agent evolutionary strategies using pytorch.
PPO-PyTorch - Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch
HandyRL - HandyRL is a handy and simple framework based on Python and PyTorch for distributed reinforcement learning that is applicable to your own environments.
de-torch - Minimal PyTorch Library for Differential Evolution