Deep Reinforcement Learning Nanodegree
Repo for the Deep Reinforcement Learning Nanodegree program.
Table of Contents
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.
- 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, the projects are reviewed by Udacity experts. 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. (Coming soon!)
- Collaboration and Competition: In the third project, you will train a pair of agents to play tennis! (Coming soon!)
OpenAI Gym Benchmarks
Acrobot-v1with Tile Coding and Q-Learning
Cartpole-v0with Hill Climbing | solved in 13 episodes
Cartpole-v0with REINFORCE | solved in 691 episodes
MountainCarContinuous-v0with Cross-Entropy Method | solved in 47 iterations
MountainCar-v0with Uniform-Grid Discretization and Q-Learning | solved in <50000 episodes
Pendulum-v0with Deep Deterministic Policy Gradients (DDPG)
BipedalWalker-v2with Deep Deterministic Policy Gradients (DDPG)
CarRacing-v0with Deep Q-Networks (DQN) | Coming soon!
LunarLander-v2with Deep Q-Networks (DQN) | solved in 1504 episodes
FrozenLake-v0with Dynamic Programming
Blackjack-v0with Monte Carlo Methods
CliffWalking-v0with Temporal-Difference Methods
To set up your python environment to run the code in this repository, follow the instructions below.
Create (and activate) a new environment with Python 3.6.
- Linux or Mac:
conda create --name drlnd python=3.6 source activate drlnd
conda create --name drlnd python=3.6 activate drlnd
Follow the instructions in this repository to perform a minimal install of OpenAI gym.
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 .
- Create an IPython kernel for the
python -m ipykernel install --user --name drlnd --display-name "drlnd"
- Before running code in a notebook, change the kernel to match the
drlndenvironment by using the drop-down