drq
pytorch-a2c-ppo-acktr-gail
| drq | pytorch-a2c-ppo-acktr-gail | |
|---|---|---|
| 1 | 3 | |
| 398 | 3,423 | |
| - | - | |
| 0.0 | 0.0 | |
| over 1 year ago | almost 2 years ago | |
| Jupyter Notebook | Python | |
| MIT License | MIT License |
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drq
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?
exorl - ExORL: Exploratory Data for Offline Reinforcement Learning
Super-mario-bros-PPO-pytorch - Proximal Policy Optimization (PPO) algorithm for Super Mario Bros
muzero-general - MuZero
soft-actor-critic - Implementation of the Soft Actor Critic algorithm using Pytorch.
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
dm_control - Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.
policy-adaptation-during-deployment - Training code and evaluation benchmarks for the "Self-Supervised Policy Adaptation during Deployment" paper.
tensorforce - Tensorforce: a TensorFlow library for applied reinforcement learning
Note - Easily implement parallel training and distributed training. Machine learning library. Note.neuralnetwork.tf package include Llama2, Llama3, Gemma, CLIP, ViT, ConvNeXt, Segformer, etc, these models built with Note are compatible with TensorFlow and can be trained with TensorFlow.
TensorFlow2.0-for-Deep-Reinforcement-Learning - TensorFlow 2.0 for Deep Reinforcement Learning. :octopus:
PCGrad - Code for "Gradient Surgery for Multi-Task Learning"
DI-engine - OpenDILab Decision AI Engine