policy-adaptation-during-deployment
pytorch-a2c-ppo-acktr-gail
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| policy-adaptation-during-deployment | pytorch-a2c-ppo-acktr-gail | |
|---|---|---|
| 1 | 3 | |
| 109 | 3,423 | |
| - | - | |
| 1.8 | 0.0 | |
| over 3 years ago | almost 2 years ago | |
| Python | Python | |
| - | MIT License |
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policy-adaptation-during-deployment
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Exploring Self-Supervised Policy Adaptation To Continue Training After Deployment Without Using Any Rewards
Code: https://github.com/nicklashansen/policy-adaptation-during-deployment
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?
Ne2Ne-Image-Denoising - Deep Unsupervised Image Denoising, based on Neighbour2Neighbour training
soft-actor-critic - Implementation of the Soft Actor Critic algorithm using Pytorch.
envpool - C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.
Super-mario-bros-PPO-pytorch - Proximal Policy Optimization (PPO) algorithm for Super Mario Bros
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
dm_control - Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.
drl_grasping - Deep Reinforcement Learning for Robotic Grasping from Octrees
tensorforce - Tensorforce: a TensorFlow library for applied reinforcement learning
dmc2gymnasium - Gymnasium integration for the DeepMind Control (DMC) suite
TensorFlow2.0-for-Deep-Reinforcement-Learning - TensorFlow 2.0 for Deep Reinforcement Learning. :octopus:
es_pytorch - High performance implementation of Deep neuroevolution in pytorch using mpi4py. Intended for use on HPC clusters
PCGrad - Code for "Gradient Surgery for Multi-Task Learning"