6 Deep RL in practice
Excellent blog post from Alex Irpan on the limitations of deep RL: https://www.alexirpan.com/2018/02/14/rl-hard.html
Another documented critic on deep RL: https://thegradient.pub/why-rl-is-flawed/
6.2 Simulation environments
Standard RL environments are needed to better compare the performance of RL algorithms. Below is a list of the most popular ones.
- OpenAI Gym https://gym.openai.com: a standard toolkit for comparing RL algorithms provided by the OpenAI foundation. It provides many environments, from the classical toy problems in RL (GridWorld, pole-balancing) to more advanced problems (Mujoco simulated robots, Atari games, Minecraft…). The main advantage is the simplicity of the interface: the user only needs to select which task he wants to solve, and a simple for loop allows to perform actions and observe their consequences:
OpenAI Universe https://universe.openai.com: a similar framework from OpenAI, but to control realistic video games (GTA V, etc).
Darts environment https://github.com/DartEnv/dart-env: a fork of gym to use the Darts simulator instead of Mujoco.
Roboschool https://github.com/openai/roboschool: another alternative to Mujoco for continuous robotic control, this time from openAI.
NIPS 2017 musculo-skeletal challenge https://github.com/stanfordnmbl/osim-rl
Deepmind Lab https://github.com/deepmind/lab: a 3D learning environment based on id Software’s Quake III Arena via ioquake3 and other open source software.
AnimalAI Olympics https://github.com/beyretb/AnimalAI-Olympics, a gym-like environment aimed at confronting RL algorithms to typical tasks in the animal cognition literature.
6.3 Algorithm implementations
State-of-the-art algorithms in deep RL are already implemented and freely available on the internet. Below is a preliminary list of the most popular ones. Most of them rely on tensorflow or keras for training the neural networks and interact directly with gym-like interfaces.
https://github.com/ShangtongZhang/reinforcement-learning-an-introduction: all the exercises in Python of the (Sutton and Barto, 2017) book.
rl-codehttps://github.com/rlcode/reinforcement-learning: many code samples for simple RL problems (GridWorld, Cartpole, Atari Games). The code samples are mostly for educational purpose (Policy Iteration, Value Iteration, Monte-Carlo, SARSA, Q-learning, REINFORCE, DQN, A2C, A3C).
keras-rlhttps://github.com/matthiasplappert/keras-rl: many deep RL algorithms implemented directly in keras: DQN, DDQN, DDPG, Continuous DQN (CDQN or NAF), Cross-Entropy Method (CEM), Dueling DQN, Deep SARSA…
Coachhttps://github.com/NervanaSystems/coach from Intel Nervana also provides many state-of-the-art algorithms: DQN, DDQN, Dueling DQN, Mixed Monte Carlo (MMC), Persistent Advantage Learning (PAL), Distributional Deep Q Network, Bootstrapped Deep Q Network, N-Step Q Learning, Neural Episodic Control (NEC), Normalized Advantage Functions (NAF), Policy Gradients (PG), A3C, DDPG, Proximal Policy Optimization (PPO), Clipped Proximal Policy Optimization, Direct Future Prediction (DFP)…
OpenAI Baselineshttps://github.com/openai/baselines from OpenAI too: A2C, ACER, ACKTR, DDPG, DQN, PPO, TRPO…
chainer-rlhttps://github.com/chainer/chainerrl implemented in Chainer (an alternative to tensorflow): A3C, ACER, Categorical DQN; DQN (including Double DQN, Persistent Advantage Learning (PAL), Double PAL, Dynamic Policy Programming (DPP)), DDPG, , PGT (Policy Gradient Theorem), PCL (Path Consistency Learning), PPO, TRPO.