How to install Keras-RL and try reinforcement learning with cart pole

At first, install keras:
Install Keras on Linux mint

Then install necessary packages:

$ python3.5 -m pip install keras-rl
$ python3.5 -m pip install gym
$ python3.5 -m pip install gym[all]

You might need libav-tools too:

$ sudo apt-get install libav-tools

Create a text file and copy and paste the following code inside:

import numpy as np
import gym

from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten
from keras.optimizers import Adam

from rl.agents.dqn import DQNAgent
from rl.policy import BoltzmannQPolicy
from rl.memory import SequentialMemory


ENV_NAME = 'CartPole-v0'


# Get the environment and extract the number of actions.
env = gym.make(ENV_NAME)
np.random.seed(123)
env.seed(123)
nb_actions = env.action_space.n

# Next, we build a very simple model.
model = Sequential()
model.add(Flatten(input_shape=(1,) + env.observation_space.shape))
model.add(Dense(16))
model.add(Activation('relu'))
model.add(Dense(16))
model.add(Activation('relu'))
model.add(Dense(16))
model.add(Activation('relu'))
model.add(Dense(nb_actions))
model.add(Activation('linear'))
print(model.summary())

# Finally, we configure and compile our agent. You can use every built-in Keras optimizer and
# even the metrics!
memory = SequentialMemory(limit=50000, window_length=1)
policy = BoltzmannQPolicy()
dqn = DQNAgent(model=model, nb_actions=nb_actions, memory=memory, nb_steps_warmup=10,
               target_model_update=1e-2, policy=policy)
dqn.compile(Adam(lr=1e-3), metrics=['mae'])

# Okay, now it's time to learn something! We visualize the training here for show, but this
# slows down training quite a lot. You can always safely abort the training prematurely using
# Ctrl + C.
dqn.fit(env, nb_steps=50000, visualize=True, verbose=2)

# After training is done, we save the final weights.
dqn.save_weights('dqn_{}_weights.h5f'.format(ENV_NAME), overwrite=True)

# Finally, evaluate our algorithm for 5 episodes.
dqn.test(env, nb_episodes=5, visualize=True)

And save it as "test.py".

Run the script:

$ sudo python3 test.py

Reinforcement learning of cart pole will start:

Other examples:
https://github.com/matthiasplappert/keras-rl/tree/master/examples


Refereces

matthiasplappert/keras-rl examples (cartpole)
https://github.com/matthiasplappert/keras-rl/tree/master/examples