The core of the algorithm, namely the computational graph and the training (and evaluation) loop, is implemented in the DQN function, which takes the name of the environment and all the other hyperparameters as arguments:
def DQN(env_name, hidden_sizes=[32], lr=1e-2, num_epochs=2000, buffer_size=100000, discount=0.99, update_target_net=1000, batch_size=64, update_freq=4, frames_num=2, min_buffer_size=5000, test_frequency=20, start_explor=1, end_explor=0.1, explor_steps=100000): env = make_env(env_name, frames_num=frames_num, skip_frames=True, noop_num=20) env_test = make_env(env_name, frames_num=frames_num, skip_frames=True, noop_num=20) env_test = gym.wrappers.Monitor(env_test, "VIDEOS/TEST_VIDEOS ...
