https://github.com/awjuliani/deeprl-agents

A set of Deep Reinforcement Learning Agents implemented in Tensorflow.
https://github.com/awjuliani/deeprl-agents

reinforcement-learning tensorflow

Last synced: about 16 hours ago
JSON representation

A set of Deep Reinforcement Learning Agents implemented in Tensorflow.

• Host: GitHub
• URL: https://github.com/awjuliani/deeprl-agents
• Owner: awjuliani
• License: mit
• Created: 2016-06-14T22:25:31.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2019-02-12T17:26:26.000Z (almost 6 years ago)
• Last Synced: 2024-10-29T17:48:53.509Z (2 months ago)
• Topics: reinforcement-learning, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 352 KB
• Stars: 2,239
• Watchers: 119
• Forks: 826
• Open Issues: 45
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Deep Reinforcement Learning Agents

This repository contains a collection of reinforcement learning algorithms written in Tensorflow. The ipython notebook here were written to go

along with a still-underway tutorial series I have been publishing on [Medium](https://medium.com/@awjuliani/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0#.4gyadb8a4).

If you are new to reinforcement learning, I recommend reading the accompanying post for each algorithm.

The repository currently contains the following algorithms:

* **Q-Table** - An implementation of Q-learning using tables to solve a stochastic environment problem.

* **Q-Network** - A neural network implementation of Q-Learning to solve the same environment as in Q-Table.

* **Simple-Policy** - An implementation of policy gradient method for stateless environments such as n-armed bandit problems.

* **Contextual-Policy** - An implementation of policy gradient method for stateful environments such as contextual bandit problems.

* **Policy-Network** - An implementation of a neural network policy-gradient agent that solves full RL problems with states and delayed rewards, and two opposite actions (ie. CartPole or Pong).

* **Vanilla-Policy** - An implementation of a neural network vanilla-policy-gradient agent that solves full RL problems with states, delayed rewards, and an arbitrary number of actions.

* **Model-Network** - An addition to the Policy-Network algorithm which includes a separate network which models the environment dynamics.

* **Double-Dueling-DQN** - An implementation of a Deep-Q Network with the Double DQN and Dueling DQN additions to improve stability and performance.

* **Deep-Recurrent-Q-Network** - An implementation of a Deep Recurrent Q-Network which can solve reinforcement learning problems involving partial observability.

* **Q-Exploration** - An implementation of DQN containing multiple action-selection strategies for exploration. Strategies include: greedy, random, e-greedy, Boltzmann, and Bayesian Dropout.

* **A3C-Doom** - An implementation of Asynchronous Advantage Actor-Critic (A3C) algorithm. It utilizes multiple agents to collectively improve a policy. This implementation can solve RL problems in 3D environments such as VizDoom challenges.