callback1 = FileLogger(filepath='save/history1_{}'.format(timenow), interval=1)
callback2 = FileLogger(filepath='save/history2_{}'.format(timenow), interval=1)
callback3 = FileLogger(filepath='save/history3_{}'.format(timenow), interval=1)
callback4 = FileLogger(filepath='save/history4_{}'.format(timenow), interval=1)
memory = SequentialMemory(limit=50000, 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=1000,
nb_steps_warmup_actor=1000,
batch_size=32,
random_process=random_process,
gamma=.99,
target_model_update=1e-3)
agent.compile([Adam(lr=0.00001, clipnorm=1.),
Adam(lr=0.01, clipnorm=1.)],
metrics=['mae'])
agent.fit(env,
nb_steps=200000,
visualize=False,
callbacks=[callback3],
verbose=2)
#time.sleep(1800)
callback1 = FileLogger(filepath='save/history1_{}'.format(timenow), interval=1)
#callback2 = FileLogger(filepath='save/history2_{}'.format(timenow), interval=1)
#callback3 = FileLogger(filepath='save/history3_{}'.format(timenow), interval=1)
#callback4 = FileLogger(filepath='save/history4_{}'.format(timenow), interval=1)
memory = SequentialMemory(limit=50000, 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=1000,
nb_steps_warmup_actor=1000,
batch_size=96,
random_process=random_process,
gamma=.99,
target_model_update=1e-3)
agent.compile([Adam(lr=0.00001), Adam(lr=0.001)], metrics=['mae'])
agent.fit(env,
nb_epsteps=3000,
visualize=False,
callbacks=[callback1],
verbose=2)
agent.save_weights('save/ddpg_{}_weights.h5f'.format(ENV_NAME), overwrite=True)
print('weights saved!')
# Memory
memory = SimpleMemory(env=env, limit=100000)
#memory = SequentialMemory(limit=10000, window_length=1)
# Noise
#random_process = OrnsteinUhlenbeckProcess(size=env.action_space.dim, theta=.15, mu=0., sigma=3.)
random_process = GaussianWhiteNoiseProcess()
callback1 = FileLogger(filepath='save/history1_{}'.format(timenow), interval=1)
# Agent
agent = DDPGAgent(
experiment=experiment,
actor=actor,
critic=critic,
env=env,
memory=memory,
#random_process=random_process
)
agent.compile()
history = agent.train(
env=env,
episodes=3000,
render=False,
callbacks=[callback1],
verbosity=2,
plots=False)
test = agent.test(env=env)
agent.save_weights('save/ddpg_ContinuousCartpole_weights.h5f', overwrite=True)