Independent and minimal implementations of some reinforcement learning algorithms using PyTorch (including PPO, A3C, A2C, ...).

Last update: Dec 31, 2022

Overview

PyTorch RL Minimal Implementations

There are implementations of some reinforcement learning algorithms, whose characteristics are as follow:

Less packages-based: Only PyTorch and Gym, for building neural networks and testing algorithms' performance respectively, are necessary to install.
Independent implementation: All RL algorithms are implemented in separate files, which facilitates to understand their processes and modify them to adapt to other tasks.
Various expansion configurations: It's convenient to configure various parameters and tools, such as reward normalization, advantage normalization, tensorboard, tqdm and so on.

RL Algorithms List

Name	Type	Estimator	Paper	File
Q-Learning	Value-based / Off policy	TD	Watkins et al. Q-Learning. Machine Learning, 1992	q_learning.py
REINFORCE	Policy-based On policy	MC	Sutton et al. Policy Gradient Methods for Reinforcement Learning with Function Approximation. In NeurIPS, 2000.	reinforce.py
DQN	Value-based / Off policy	TD	Mnih et al. Human-level control through deep reinforcement learning. Nature, 2015.	doing
A2C	Actor-Critic / On policy	n-step TD	Mnih et al. Asynchronous Methods for Deep Reinforcement Learning. In ICML, 2016.	a2c.py
A3C	Actor-Critic / On policy	n-step TD	.Mnih et al. Asynchronous Methods for Deep Reinforcement Learning. In ICML, 2016	a3c.py
ACER	Actor-Critic / On policy	GAE	Wang et al. Sample Efficient Actor-Critic with Experience Replay. In ICLR, 2017.	doing
ACKTR	Actor-Critic / On policy	GAE	Wu et al. Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation. In NeurIPS, 2017.	doing
PPO	Actor-Critic / On policy	GAE	Schulman et al. Proximal Policy Optimization Algorithms. arXiv, 2017.	ppo.py

Quick Start

Requirements

pytorch
gym

tensorboard  # for summary writer
tqdm         # for process bar

Abstract Agent

Components / Parameters

Component	Description
policy	neural network model
gamma	discount factor of cumulative reward
lr	learning rate. i.e. `lr_actor`, `lr_critic`
lr_decay	weight decay to schedule the learning rate
lr_scheduler	scheduler for the learning rate
coef_critic_loss	coefficient of critic loss
coef_entropy_loss	coefficient of entropy loss
writer	summary writer to record information
buffer	replay buffer to store historical trajectories
use_cuda	use GPU
clip_grad	gradients clipping
max_grad_norm	maximum norm of gradients clipped
norm_advantage	advantage normalization
open_tb	open summary writer
open_tqdm	open process bar

Methods

Methods	Description
preprocess_obs()	preprocess observation before input into the neural network
select_action()	use actor network to select an action based on the policy distribution.
estimate_obs()	use critic network to estimate the value of observation
update()	update the parameter by calculate losses and gradients
train()	set the neural network to train mode
eval()	set the neural network to evaluate mode
save()	save the model parameters
load()	load the model parameters

Update & To-do & Limitations

Update History

2021-12-09 ADD TRICK:norm_critic_loss in PPO
2021-12-09 ADD PARAM: coef_critic_loss, coef_entropy_loss, log_step
2021-12-07 ADD ALGO: A3C
2021-12-05 ADD ALGO: PPO
2021-11-28 ADD ALGO: A2C
2021-11-20 ADD ALGO: Q learning, Reinforce

Independent and minimal implementations of some reinforcement learning algorithms using PyTorch (including PPO, A3C, A2C, ...).

Related tags

Overview

PyTorch RL Minimal Implementations

RL Algorithms List

Quick Start

Requirements

Abstract Agent

Components / Parameters

Methods

Update & To-do & Limitations

Update History

To-do List

Current Limitations

Reference & Acknowledgements

Owner

Gemini Light

Implementation of Convolutional enhanced image Transformer

Simulating an AI playing 2048 using the Expectimax algorithm

Official implementation for: Blended Diffusion for Text-driven Editing of Natural Images.

Contrastive Loss Gradient Attack (CLGA)

Contrastive Fact Verification

RIFE - Real-Time Intermediate Flow Estimation for Video Frame Interpolation

Codebase for arXiv preprint "NeRF++: Analyzing and Improving Neural Radiance Fields"

The source code of the ICCV2021 paper "PIRenderer: Controllable Portrait Image Generation via Semantic Neural Rendering"

IJCAI2020 & IJCV 2020 :city_sunrise: Unsupervised Scene Adaptation with Memory Regularization in vivo

AI-Fitness-Tracker - AI Fitness Tracker With Python

一些经典的CTR算法的复现; LR, FM, FFM, AFM, DeepFM，xDeepFM, PNN, DCN, DCNv2, DIFM, AutoInt, FiBiNet,AFN,ONN,DIN, DIEN ... （pytorch, tf2.0）

PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud, CVPR 2019.

This is a collection of simple PyTorch implementations of neural networks and related algorithms. These implementations are documented with explanations,

Combine Tacotron2 and Hifi GAN to generate speech from text

Publication describing 3 ML examples at NSLS-II and interfacing into Bluesky

Reproduces the results of the paper "Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations".

Learning Tracking Representations via Dual-Branch Fully Transformer Networks

Object recognition using Azure Custom Vision AI and Azure Functions

Denoising images with Fourier Ring Correlation loss

Python Assignments for the Deep Learning lectures by Andrew NG on coursera with complete submission for grading capability.