PPO-for-Beginners VS R-NaD

Compare PPO-for-Beginners vs R-NaD and see what are their differences.

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. (by ericyangyu)
Ppo reinforcement-learning reinforcement-learning-algorithms Machine Learning Pytorch
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R-NaD

Experimentation with Regularized Nash Dynamics on a GPU accelerated game (by baskuit)
multiagent-reinforcement-learning Pytorch reinforcement-learning reinforcement-learning-algorithms deepnash rnad
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PPO-for-Beginners R-NaD
1 1
653 30
- -
4.2 4.7
5 months ago about 1 year ago
Python Python
MIT License Apache License 2.0
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PPO-for-Beginners

Posts with mentions or reviews of PPO-for-Beginners. We have used some of these posts to build our list of alternatives and similar projects.

R-NaD

Posts with mentions or reviews of R-NaD. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing PPO-for-Beginners and R-NaD you can also consider the following projects:

stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

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.

PantheonRL - PantheonRL is a package for training and testing multi-agent reinforcement learning environments. PantheonRL supports cross-play, fine-tuning, ad-hoc coordination, and more.

PPO-PyTorch - Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch

Simple-MADRL-Chess - MADRL project solving chess environment using PPO with two different methods: 2 agents/networks and a single agent/network.

cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)

stable-baselines3-contrib - Contrib package for Stable-Baselines3 - Experimental reinforcement learning (RL) code

warp-drive - Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning Framework on a GPU (JMLR 2022)

PPO-for-Beginners vs stable-baselines3 R-NaD vs pytorch-learn-reinforcement-learning PPO-for-Beginners vs pytorch-learn-reinforcement-learning R-NaD vs PantheonRL PPO-for-Beginners vs PPO-PyTorch R-NaD vs Simple-MADRL-Chess PPO-for-Beginners vs cleanrl R-NaD vs stable-baselines3 PPO-for-Beginners vs stable-baselines3-contrib R-NaD vs warp-drive