pytorch-a2c-ppo-acktr-gail
autonomous-learning-library
| pytorch-a2c-ppo-acktr-gail | autonomous-learning-library | |
|---|---|---|
| 3 | 2 | |
| 3,423 | 639 | |
| - | - | |
| 0.0 | 7.6 | |
| almost 2 years ago | about 2 months ago | |
| Python | Python | |
| MIT License | MIT License |
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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).
autonomous-learning-library
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What's the best "Non-Black Box" framework for SOTA algorithms?
I find Autonomous Learning Library well-designed and clean, despite its modularity to some degree.
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Where do people get their algorithm implementations from?
I very strongly recommend the autonomous learning library: https://github.com/cpnota/autonomous-learning-library
What are some alternatives?
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.
deep_rl_zoo - A collection of Deep Reinforcement Learning algorithms implemented with PyTorch to solve Atari games and classic control tasks like CartPole, LunarLander, and MountainCar.
dm_control - Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.
learning-to-drive-in-5-minutes - Implementation of reinforcement learning approach to make a car learn to drive smoothly in minutes
tensorforce - Tensorforce: a TensorFlow library for applied reinforcement learning
Tetris-deep-Q-learning-pytorch - Deep Q-learning for playing tetris game
TensorFlow2.0-for-Deep-Reinforcement-Learning - TensorFlow 2.0 for Deep Reinforcement Learning. :octopus:
Meta-SAC - Auto-tune the Entropy Temperature of Soft Actor-Critic via Metagradient - 7th ICML AutoML workshop 2020
PCGrad - Code for "Gradient Surgery for Multi-Task Learning"
Fleet-AI - Using Reinforcement Learning to play Battleship