p.add_argument('--replay_size', type=int, default=1000000)
# Other stuff
p.add_argument('--batch_size', type=int, default=64)
p.add_argument('--do_not_save', action='store_true')
p.add_argument('--n_iter', type=int, default=1000)
p.add_argument('--seed', type=int, default=0)
args = p.parse_args()
# Handle the log directory and save the arguments.
logdir = 'outputs/' + args.envname + '/seed' + str(args.seed).zfill(2)
if args.do_not_save:
logdir = None
logz.configure_output_dir(logdir)
if logdir is not None:
with open(logdir + '/args.pkl', 'wb') as f:
pickle.dump(args, f)
print("Saving in logdir: {}".format(logdir))
# Other stuff for seeding and getting things set up.
tf.set_random_seed(args.seed)
np.random.seed(args.seed)
env = gym.make(args.envname)
tf_config = tf.ConfigProto(inter_op_parallelism_threads=1,
intra_op_parallelism_threads=1)
sess = tf.Session(config=tf_config)
ddpg = DDPGAgent(sess, env, args)
ddpg.train()
ddpg.test()
actor.add(Activation('relu'))
actor.add(Dense(nb_actions))
actor.add(Activation('sigmoid'))
action_input = Input(shape=(nb_actions,), name='action_input')
observation_input = Input(shape=(1,) + (11,), name='observation_input')
flattened_observation = Flatten()(observation_input)
x = Concatenate()([action_input, flattened_observation])
x = Dense(32)(x)
x = Activation('relu')(x)
x = Dense(32)(x)
x = Activation('relu')(x)
x = Dense(32)(x)
x = Activation('relu')(x)
x = Dense(1)(x)
x = Activation('linear')(x)
critic = Model(inputs=[action_input, observation_input], outputs=x)
memory = SequentialMemory(limit=100000, window_length=1)
random_process = OrnsteinUhlenbeckProcess(size=nb_actions, theta=.15, mu=0., sigma=.1)
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=10,
random_process=random_process, gamma=.995, target_model_update=1e-3)
agent.compile(Adam(lr=.0005, clipnorm=1.), metrics=['mae'])
agent.fit(env, nb_steps=10000, 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=15, visualize=False, nb_max_episode_steps=95, plt="")
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_")
