Deep-Reinforcement-Learning-Hands-On
PPO-PyTorch
| Deep-Reinforcement-Learning-Hands-On | PPO-PyTorch | |
|---|---|---|
| 2 | 2 | |
| 2,746 | 1,472 | |
| 0.0% | - | |
| 0.0 | 2.8 | |
| about 1 year ago | 5 months ago | |
| Python | Python | |
| MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Deep-Reinforcement-Learning-Hands-On
-
A2C/PPO with continuous action space
In some methods, like the one here, the actor network has two heads, one for the mean and one for the variance. In other methods, like the one here, the network only outputs the mean, while the variance is pre-defined and is decaying throughout the training.
-
Beginner attempting to implement Noisy DQN
https://github.com/PacktPublishing/Deep-Reinforcement-Learning-Hands-On/blob/baa9d013596ea8ea8ed6826b9de6679d98b897ca/Chapter07/lib/dqn_model.py#L9
PPO-PyTorch
-
Where does the loss function for Policy Gradient come from?
It's just very convient implementation wise, in just a few lines you can get the "loss": (from https://github.com/nikhilbarhate99/PPO-PyTorch/blob/master/PPO.py)
-
A2C/PPO with continuous action space
In some methods, like the one here, the actor network has two heads, one for the mean and one for the variance. In other methods, like the one here, the network only outputs the mean, while the variance is pre-defined and is decaying throughout the training.
What are some alternatives?
TensorFlow2.0-for-Deep-Reinforcement-Learning - TensorFlow 2.0 for Deep Reinforcement Learning. :octopus:
HandyRL - HandyRL is a handy and simple framework based on Python and PyTorch for distributed reinforcement learning that is applicable to your own environments.
l2rpn-baselines - L2RPN Baselines a repository to host baselines for l2rpn competitions.
Pytorch-PCGrad - Pytorch reimplementation for "Gradient Surgery for Multi-Task Learning"
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
pytorch-accelerated - A lightweight library designed to accelerate the process of training PyTorch models by providing a minimal, but extensible training loop which is flexible enough to handle the majority of use cases, and capable of utilizing different hardware options with no code changes required. Docs: https://pytorch-accelerated.readthedocs.io/en/latest/
nes-torch - Minimal PyTorch Library for Natural Evolution Strategies
autonomous-learning-library - A PyTorch library for building deep reinforcement learning agents.
recurrent-ppo-truncated-bptt - Baseline implementation of recurrent PPO using truncated BPTT
PPO-for-Beginners - A simple and well styled PPO implementation. Based on my Medium series: https://medium.com/@eyyu/coding-ppo-from-scratch-with-pytorch-part-1-4-613dfc1b14c8.
Simple-MADRL-Chess - MADRL project solving chess environment using PPO with two different methods: 2 agents/networks and a single agent/network.
DeepCubeA - Code for DeepCubeA, a Deep Reinforcement Learning algorithm that can learn to solve the Rubik's cube.