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RLToolkit

Reinforcement learning library for industry and personal use.

RLToolkit is a python package intended to solve reinforcement learning environments by having users make environments rather than complicated reinforcement learning methods.

If you can provide an environment and a neural network, RLToolkit attempts to solve the environment for you.

Installation

Download RLToolkit

git clone https://github.com/BlakeERichey/RLToolkit

Install

cd RLToolkit
pip install -e .

Usage

The RLToolkit requires three criteria when training an AI.

  1. Gym environment to solve
  2. Keras neural network
  3. Reinforcement learning method (Provided by RLToolkit)

Environments

Gym is a python package that offers extensive documetation and tools for developing robust and highly structured environments that permits assurances when RLToolkit trains an AI.

Useful links:

Once you find a gym environment you would like to solve, initialize it like so env = gym.make('YourEnvironmentName')

Neural Networks

Keras is a high level API for easy-to-build neural network development.
We recommend you you look at Keras Documentation for a comprehensive guide, but some basic neural network examples are shown below (comments for clarity).

Artificial Neural Network

  model = Sequential()
  model.add(Dense(256, activation='relu', input_shape=env.observation_space.shape)) #add input layer
  model.add(Dense(64, activation='tanh'))                   #add hidden layer
  model.add(Dense(256, activation='relu'))                  #change activation method
  model.add(Dense(64, activation='relu'))                   #another hidden layer
  model.add(Dense(env.action_space.n, activation='linear')) #add output layer
  model.compile(Adam(0.001), loss='mse')                    #compile network
  model.summary()                                           #show network architecture

Convolutional Neural Network

  model = Sequential()
  #Input layer
  model.add(Conv2D(64, kernel_size=3, activation='relu', input_shape=env.observation_space.shape))

  #Add hidden layer
  model.add(Conv2D(32, kernel_size=3, activation='relu'))
  
  #Applying pooling
  model.add(MaxPooling2D(pool_size=(2,2)))
  
  #Add FCN
  model.add(Flatten())
  model.add(Dense(env.action_space.n, activation='softmax'))

  #compile network
  model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

Time Distributed (LSTM) Neural Network

  n_timesteps = 5
  model = Sequential()
  
  #Input layer
  model.add(LSTM(64, activation='relu', \
    input_shape=((n_timesteps,) + env.observation_space.shape), return_sequences=True))
  
  #add hidden layers
  model.add(LSTM(128, return_sequences=True, activation='tanh'))
  model.add(LSTM(32, return_sequences=True, dropout=.2, activation='relu'))
  
  #output layer
  model.add(LSTM(env.action_space.n))
  
  #compile network
  model.compile(loss="mse", optimizer=Adam(lr=0.001)) 
  
  #show network architecture
  model.summary()

Reinforcement Learning Method

RLToolkit offers multiple methods you can use to solve an environment. Each has their own list of requirements and documentation. All you need to do is choose which method you want and learn its parameters.

Included Reinforcement learning methods:

  • Deep Q Learning
  • NeuroEvolution

Future Implementations:

  • A3C
  • Policy Gradient

You can initialize a method like so:
method = DQL(rb_size=500, replay_batch_size=32)

More examples of initializing methods can be found in examples

Solve the Environment

Once an environment, neural network, and method have been chose you can create your AI easily:

  import gym
  import keras
  from keras.models import Sequential
  from keras.layers import Dense
  from keras.optimizers import Adam
  from rltoolkit.methods import DQL

  #Initialize environment
  env = gym.make('CartPole-v0')

  #Build network
  model = Sequential()
  model.add(Dense(16, activation='relu', input_shape=env.observation_space.shape))
  model.add(Dense(64, activation='relu'))
  model.add(Dense(2, activation='linear'))
  model.compile(Adam(0.001), loss='mse')
  model.summary()

  #Initialize Deep Q Learning Method
  method = DQL(rb_size=500, replay_batch_size=32)

  #Train neural network for 50 episodes
  ai = method.train(model, env, 50, epsilon_decay=.9)

Each method has its own parameters and lets you fine tune them in the method.train function call.

Test your AI

We want the make testing of your AI easy, so we provided a utility for doing so. With an AI, or Keras neural network, and a gym environment, simply call the test_network function like so:

  from rltoolkit.utils import test_network
  #Test models results for 5 episodes
  avg = test_network(model, env, episodes=5, render=True)
  print('Average after 5 episodes:', avg)