Minimal example of creating a Huskarl DQN agent and visualizing it learning how to balance a cartpole.

huskarl-minimal-dqn-cartpole.py

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
import huskarl as hk
import gym

# Setup gym environment
create_env = lambda: gym.make('CartPole-v0').unwrapped
dummy_env = create_env()

# Build a simple neural network with 3 fully connected layers as our model
model = Sequential([
  Dense(16, activation='relu', input_shape=dummy_env.observation_space.shape),
  Dense(16, activation='relu'),
  Dense(16, activation='relu'),
])

# Create Deep Q-Learning Network agent
agent = hk.agent.DQN(model, actions=dummy_env.action_space.n, nsteps=2)

# Create simulation, train and then test
sim = hk.Simulation(create_env, agent)
sim.train(max_steps=3000, visualize=True)
sim.test(max_steps=1000)