pytorch-a2c-ppo-acktr-gail
tensorforce
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| pytorch-a2c-ppo-acktr-gail | tensorforce | |
|---|---|---|
| 3 | 1 | |
| 3,423 | 3,280 | |
| - | 0.2% | |
| 0.0 | 3.0 | |
| almost 2 years ago | 19 days ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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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).
tensorforce
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Advice on doing RL for Settlers of Catan?
The most promising approach has been using the TensorForce framework (https://github.com/tensorforce/tensorforce) with a custom environment that represents a simpler game (1v1 against a bot that chooses actions randomly, no trading between players, and fixing discarding to be done automatically and at random).
What are some alternatives?
soft-actor-critic - Implementation of the Soft Actor Critic algorithm using Pytorch.
TensorFlow2.0-for-Deep-Reinforcement-Learning - TensorFlow 2.0 for Deep Reinforcement Learning. :octopus:
Super-mario-bros-PPO-pytorch - Proximal Policy Optimization (PPO) algorithm for Super Mario Bros
DeepRL-TensorFlow2 - 🐋 Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2
dm_control - Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.
agents - TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.
deepdrive - Deepdrive is a simulator that allows anyone with a PC to push the state-of-the-art in self-driving
PCGrad - Code for "Gradient Surgery for Multi-Task Learning"
trax - Trax — Deep Learning with Clear Code and Speed
DI-engine - OpenDILab Decision AI Engine
action-branching-agents - (AAAI 2018) Action Branching Architectures for Deep Reinforcement Learning