checkpoint_weights_filename = 'dqn_' + args.env_name + '_weights_{step}.h5f'
log_filename = 'dqn_{}_log.json'.format(args.env_name)
callbacks = [
ModelIntervalCheckpoint(checkpoint_weights_filename, interval=250000)
]
callbacks += [FileLogger(log_filename, interval=100)]
dqn.fit(env, callbacks=callbacks, nb_steps=1750000, log_interval=10000)
# After training is done, we save the final weights one more time.
dqn.save_weights(weights_filename, overwrite=True)
# Finally, evaluate our algorithm for 10 episodes.
dqn.test(env, nb_episodes=10, visualize=False)
elif args.mode == 'test':
weights_filename = 'dqn_{}_weights.h5f'.format(args.env_name)
if args.weights:
weights_filename = args.weights
dqn.load_weights(weights_filename)
dqn.test(env, nb_episodes=10, visualize=True)
elif args.mode == 'explore':
home = expanduser("~")
path = home + '/.costar/data/test'
callbacks = [BinaryDataLogger(path, interval=-1)]
#callbacks = [TrainEpisodeLogger1()]
dqn.policy = RandomPolicy()
dqn.fit(env,
visualize=False,
callbacks=callbacks,
nb_steps=1750000,
log_interval=10000)
value_test=.05,
nb_steps=5000)
#policy = EpsGreedyQPolicy(10)
#policy = BoltzmannQPolicy()
dqn = DQNAgent(model=model,
nb_actions=num_actions,
memory=memory,
nb_steps_warmup=window * 3,
policy=policy)
dqn.compile(Adam(lr=1e-3), metrics=['mae'])
dqn.enable_dueling_network = True
if model_exist:
dqn.load_weights(model_path)
dqn.policy = LinearAnnealedPolicy(EpsGreedyQPolicy(),
attr='eps',
value_max=.5,
value_min=.1,
value_test=.05,
nb_steps=5000)
env.set_data_interval(train_start, train_end)
train_history = dqn.fit(env,
nb_steps=5000,
visualize=False,
verbose=2,
action_repetition=5)
env.set_data_interval(train_start, test_end)
print('Whole')
train_history = dqn.test(env, nb_episodes=2)
dqn.save_weights(model_path, overwrite=True)
env.save_action_plot('action_validate.csv')
plt.axvline(x=train_end - train_start)
