Simple-MADRL-Chess
pytorch-a2c-ppo-acktr-gail
| Simple-MADRL-Chess | pytorch-a2c-ppo-acktr-gail | |
|---|---|---|
| 1 | 3 | |
| 10 | 3,423 | |
| - | - | |
| 7.2 | 0.0 | |
| about 1 year ago | almost 2 years ago | |
| Python | Python | |
| MIT License | MIT License |
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Simple-MADRL-Chess
pytorch-a2c-ppo-acktr-gail
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How does advantage estimation is done when episodes are of variable length in PPO?
As an example look at "compute_returns" function here (and pay attention to how self.masks is used): https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail/blob/master/a2c_ppo_acktr/storage.py
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How to pretrain a model on expert data?
Try using an imitation learning algorithm. Two popular options are MaxEnt IRL and GAIL. This repository has GAIL implementation and this repository has MaxEnt IRL and GAIL implementation. There are other implementations too that you can check out.
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Trying to Train PPO Agent for Pendulum-v0 from Pixel Inputs
For the PPO, I used this repo, which includes most tricks including GAE, normalized rewards, etc. I have verified this repo works for the traditional Pendulum-v0 task and Atari games (Pong and Breakout).
What are some alternatives?
warp-drive - Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning Framework on a GPU (JMLR 2022)
Super-mario-bros-PPO-pytorch - Proximal Policy Optimization (PPO) algorithm for Super Mario Bros
PPO-PyTorch - Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch
soft-actor-critic - Implementation of the Soft Actor Critic algorithm using Pytorch.
R-NaD - Experimentation with Regularized Nash Dynamics on a GPU accelerated game
dm_control - Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.
tensorforce - Tensorforce: a TensorFlow library for applied reinforcement learning
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.
TensorFlow2.0-for-Deep-Reinforcement-Learning - TensorFlow 2.0 for Deep Reinforcement Learning. :octopus:
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
PCGrad - Code for "Gradient Surgery for Multi-Task Learning"