README.md

Deep Reinforcement Learning Nanodegree

This repository contains material related to Udacity's Deep Reinforcement Learning Nanodegree program.

Table of Contents

Tutorials

The tutorials lead you through implementing various algorithms in reinforcement learning. All of the code is in PyTorch (v0.4) and Python 3.

• Dynamic Programming: Implement Dynamic Programming algorithms such as Policy Evaluation, Policy Improvement, Policy Iteration, and Value Iteration.
• Monte Carlo: Implement Monte Carlo methods for prediction and control.
• Temporal-Difference: Implement Temporal-Difference methods such as Sarsa, Q-Learning, and Expected Sarsa.
• Discretization: Learn how to discretize continuous state spaces, and solve the Mountain Car environment.
• Tile Coding: Implement a method for discretizing continuous state spaces that enables better generalization.
• Deep Q-Network: Explore how to use a Deep Q-Network (DQN) to navigate a space vehicle without crashing.
• Robotics: Use a C++ API to train reinforcement learning agents from virtual robotic simulation in 3D. (External link)
• Hill Climbing: Use hill climbing with adaptive noise scaling to balance a pole on a moving cart.
• Cross-Entropy Method: Use the cross-entropy method to train a car to navigate a steep hill.
• REINFORCE: Learn how to use Monte Carlo Policy Gradients to solve a classic control task.
• Proximal Policy Optimization: Explore how to use Proximal Policy Optimization (PPO) to solve a classic reinforcement learning task. (Coming soon!)
• Deep Deterministic Policy Gradients: Explore how to use Deep Deterministic Policy Gradients (DDPG) with OpenAI Gym environments.
  • Pendulum: Use OpenAI Gym's Pendulum environment.
  • BipedalWalker: Use OpenAI Gym's BipedalWalker environment.
• Finance: Train an agent to discover optimal trading strategies. (Coming soon!)

Labs / Projects

The labs and projects can be found below. All of the projects use rich simulation environments from Unity ML-Agents. In the Deep Reinforcement Learning Nanodegree program, you will receive a review of your project. These reviews are meant to give you personalized feedback and to tell you what can be improved in your code.

• The Taxi Problem: In this lab, you will train a taxi to pick up and drop off passengers.
• Navigation: In the first project, you will train an agent to collect yellow bananas while avoiding blue bananas.
• Continuous Control: In the second project, you will train an robotic arm to reach target locations.
• Collaboration and Competition: In the third project, you will train a pair of agents to play tennis!

Resources

• Cheatsheet: You are encouraged to use this PDF file to guide your study of reinforcement learning.

OpenAI Gym Benchmarks

Classic Control

• Acrobot-v1 with Tile Coding and Q-Learning
• Cartpole-v0 with Hill Climbing | solved in 13 episodes
• Cartpole-v0 with REINFORCE | solved in 691 episodes
• MountainCarContinuous-v0 with Cross-Entropy Method | solved in 47 iterations
• MountainCar-v0 with Uniform-Grid Discretization and Q-Learning | solved in <50000 episodes
• Pendulum-v0 with Deep Deterministic Policy Gradients (DDPG)

Box2d

• BipedalWalker-v2 with Deep Deterministic Policy Gradients (DDPG)
• CarRacing-v0 with Deep Q-Networks (DQN) | Coming soon!
• LunarLander-v2 with Deep Q-Networks (DQN) | solved in 1504 episodes

Toy Text

• FrozenLake-v0 with Dynamic Programming
• Blackjack-v0 with Monte Carlo Methods
• CliffWalking-v0 with Temporal-Difference Methods

Dependencies

To set up your python environment to run the code in this repository, follow the instructions below.

1. Create (and activate) a new environment with Python 3.6.

   • Linux or Mac:

   conda create --name drlnd python=3.6
   source activate drlnd

   • Windows:

   conda create --name drlnd python=3.6 
   activate drlnd

2. Follow the instructions in this repository to perform a minimal install of OpenAI gym.

   • Next, install the classic control environment group by following the instructions here.
   • Then, install the box2d environment group by following the instructions here.

3. Clone the repository (if you haven't already!), and navigate to the python/ folder. Then, install several dependencies.

git clone https://github.com/udacity/deep-reinforcement-learning.git
cd deep-reinforcement-learning/python
pip install .

4. Create an IPython kernel for the drlnd environment.

python -m ipykernel install --user --name drlnd --display-name "drlnd"

5. Before running code in a notebook, change the kernel to match the drlnd environment by using the drop-down Kernel menu.

Want to learn more?

Come learn with us in the Deep Reinforcement Learning Nanodegree program at Udacity!