This is a generic, easy to use, and keras-compatible deep RL framework.

It began as a fork of keras-rl but is now a separated project.

• Define your Deep Nets using Keras
• Simulate on the OpenAI Gym Environments
• Easy to implement a new algorithm, using a well-defined API
• Advanced training capabilities: Offline training, critic-only (or actor-only) training...
• Easy logging : Tensorboard, Terminal...

Here's an example of the evolution of the policy during learning on the ContinuousMountainCar environment:

An online documentation can be found at:

http://vinci.readthedocs.io/en/latest/

Run :

pip install  git+ssh://[email protected]/Phylliade/vinci.git

Vinci is designed to seamlessy use Keras's networks. You can design your networks as always, using the Sequential or Functional API.

Vinci also adds some utilities to make the network creation environment agnostic, which can be nice!

To do this, the env object (a wrapper around a gym env, of type rl.EnvWrapper) provides different utilities, depending if you're using the Sequential or Functional APIs.

You just have to design your Keras model using the functional API and the state and action placeholders of the env object.

For example, for a simple critic:

# Inputs
observation = env.state
action = env.action
# Concatenate the inputs for the critic
inputs = concatenate([observation, action])

# Hidden layer
x = Dense(100)(inputs)
x = Activation('relu')(x)

# Output layer
x = Dense(1)(x)
x = Activation('linear')(x)

# Final model
critic = Model(inputs=[observation, action], outputs=[x])

Since you have to specify the input shapes by hand, you can use the state_space_dim and action_space_dim attributes of the EnvWrapper.

For example of an actor:

actor = Sequential()

# Hidden layers
actor.add(Dense(400, input_shape=(env.state_space_dim,)))
actor.add(Activation("relu"))
actor.add(Dense(300))
actor.add(Activation("relu"))

# Output layer
actor.add(Dense(env.action_space_dim, activation="tanh"))

Internally, Keras models are used in a functional fashion:

out = keras_model(in)

Some may wonder about some potential leaks with this usage, and they're right! With a traditional function, each time keras_model(in) is called, a new Tensor is created (and every underlying ops) and added to the Graph.

But, Keras uses a cache for the computations, so each call to keras_model(in) always results in the same variable.