critic = Model(inputs=[action_input, observation_input], outputs=x)
memory = SequentialMemory(limit=1000, window_length=1)
random_process = OrnsteinUhlenbeckProcess(size=nb_actions,
theta=.15,
mu=0.,
sigma=.3)
agent = DDPGAgent(nb_actions=nb_actions,
actor=actor,
critic=critic,
critic_action_input=action_input,
memory=memory,
nb_steps_warmup_critic=100,
nb_steps_warmup_actor=300,
random_process=random_process,
gamma=.995,
target_model_update=2e-3)
agent.compile(Adam(lr=.005, clipnorm=1.), metrics=['mae'])
agent.fit(env,
nb_steps=555,
visualize=False,
verbose=0,
nb_max_episode_steps=95)
agent.save_weights('weights/ddpg_{}_weights.h5f'.format("stormwater"),
overwrite=True)
agent.test(env, nb_episodes=1, visualize=True, nb_max_episode_steps=95)
env.graph("plots/test_plot_")
env=env,
memory=(memory, episode_mem),
critic_goal_input=goal_input,
delta_clip=1.0,
nb_steps_warmup_critic=1000,
nb_steps_warmup_actor=1000,
random_process=random_process,
gamma=0.98,
target_model_update=0.001,
val_goal=args.val_goal,
vectorized=args.vectorized)
agent.compile([Adam(lr=1e-4), Adam(lr=1e-3)], metrics=['mae'])
agent.fit(env,
nb_steps=200000,
folder_to_save=folder_to_save,
visualize=False,
verbose=1,
reward_type=reward_type,
nb_max_episode_steps=50,
max_tau=12)
agent.save_weights(os.path.join(
folder_to_save, 'ddpg_{}_weights_{}.h5f'.format(ENV_NAME,
reward_type)),
overwrite=True)
sys.exit()
# Finally, evaluate our algorithm for 5 episodes.
agent.test(env, nb_episodes=5, visualize=True, nb_max_episode_steps=50)
