https://github.com/alexllor1991/reinforcement-learning
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
Science Score: 23.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
○codemeta.json file
-
○.zenodo.json file
-
✓DOI references
Found 1 DOI reference(s) in README -
✓Academic publication links
Links to: arxiv.org -
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (7.1%) to scientific vocabulary
Repository
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
Basic Info
- Host: GitHub
- Owner: alexllor1991
- License: mit
- Language: Jupyter Notebook
- Default Branch: master
- Homepage: http://www.wildml.com/2016/10/learning-reinforcement-learning/
- Size: 5.22 MB
Statistics
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Overview
This repository provides code, exercises and solutions for popular Reinforcement Learning algorithms. These are meant to serve as a learning tool to complement the theoretical materials from
Each folder in corresponds to one or more chapters of the above textbook and/or course. In addition to exercises and solution, each folder also contains a list of learning goals, a brief concept summary, and links to the relevant readings.
All code is written in Python 3 and uses RL environments from OpenAI Gym. Advanced techniques use Tensorflow for neural network implementations.
Table of Contents
- Introduction to RL problems & OpenAI Gym
- MDPs and Bellman Equations
- Dynamic Programming: Model-Based RL, Policy Iteration and Value Iteration
- Monte Carlo Model-Free Prediction & Control
- Temporal Difference Model-Free Prediction & Control
- Function Approximation
- Deep Q Learning (WIP)
- Policy Gradient Methods (WIP)
- Learning and Planning (WIP)
- Exploration and Exploitation (WIP)
List of Implemented Algorithms
- Dynamic Programming Policy Evaluation
- Dynamic Programming Policy Iteration
- Dynamic Programming Value Iteration
- Monte Carlo Prediction
- Monte Carlo Control with Epsilon-Greedy Policies
- Monte Carlo Off-Policy Control with Importance Sampling
- SARSA (On Policy TD Learning)
- Q-Learning (Off Policy TD Learning)
- Q-Learning with Linear Function Approximation
- Deep Q-Learning for Atari Games
- Double Deep-Q Learning for Atari Games
- Deep Q-Learning with Prioritized Experience Replay (WIP)
- Policy Gradient: REINFORCE with Baseline
- Policy Gradient: Actor Critic with Baseline
- Policy Gradient: Actor Critic with Baseline for Continuous Action Spaces
- Deterministic Policy Gradients for Continuous Action Spaces (WIP)
- Deep Deterministic Policy Gradients (DDPG) (WIP)
- Asynchronous Advantage Actor Critic (A3C)
Resources
Textbooks:
Classes:
- David Silver's Reinforcement Learning Course (UCL, 2015)
- CS294 - Deep Reinforcement Learning (Berkeley, Fall 2015)
- CS 8803 - Reinforcement Learning (Georgia Tech)
- CS885 - Reinforcement Learning (UWaterloo), Spring 2018
- CS294-112 - Deep Reinforcement Learning (UC Berkeley)
Talks/Tutorials:
- Introduction to Reinforcement Learning (Joelle Pineau @ Deep Learning Summer School 2016)
- Deep Reinforcement Learning (Pieter Abbeel @ Deep Learning Summer School 2016)
- Deep Reinforcement Learning ICML 2016 Tutorial (David Silver)
- Tutorial: Introduction to Reinforcement Learning with Function Approximation
- John Schulman - Deep Reinforcement Learning (4 Lectures)
- Deep Reinforcement Learning Slides @ NIPS 2016
- OpenAI Spinning Up
Other Projects:
Selected Papers:
- Human-Level Control through Deep Reinforcement Learning (2015-02)
- Deep Reinforcement Learning with Double Q-learning (2015-09)
- Continuous control with deep reinforcement learning (2015-09)
- Prioritized Experience Replay (2015-11)
- Dueling Network Architectures for Deep Reinforcement Learning (2015-11)
- Asynchronous Methods for Deep Reinforcement Learning (2016-02)
- Deep Reinforcement Learning from Self-Play in Imperfect-Information Games (2016-03)
- Mastering the game of Go with deep neural networks and tree search
Owner
- Login: alexllor1991
- Kind: user
- Repositories: 7
- Profile: https://github.com/alexllor1991