DRL-robot-navigation
pytorch-a2c-ppo-acktr-gail
| DRL-robot-navigation | pytorch-a2c-ppo-acktr-gail | |
|---|---|---|
| 1 | 3 | |
| 440 | 3,423 | |
| - | - | |
| 2.1 | 0.0 | |
| 9 months ago | almost 2 years ago | |
| Python | Python | |
| MIT License | MIT License |
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DRL-robot-navigation
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It may be a dumb question but please bare with me
That's the link of the github I'm trying to convert completely to ROS2: https://github.com/reiniscimurs/DRL-robot-navigation
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?
rlalgorithms-tf2 - Packaged deep reinforcement learning algorithms in tensorflow 2.x
soft-actor-critic - Implementation of the Soft Actor Critic algorithm using Pytorch.
drl_grasping - Deep Reinforcement Learning for Robotic Grasping from Octrees
Super-mario-bros-PPO-pytorch - Proximal Policy Optimization (PPO) algorithm for Super Mario Bros
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.
machin - Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...
tensorforce - Tensorforce: a TensorFlow library for applied reinforcement learning
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
Reinforcement-Learning - Learn Deep Reinforcement Learning in 60 days! Lectures & Code in Python. Reinforcement Learning + Deep Learning