def test_cem():
env = TwoRoundDeterministicRewardEnv()
np.random.seed(123)
env.seed(123)
random.seed(123)
nb_actions = env.action_space.n
# Next, we build a very simple model.
model = Sequential()
model.add(Dense(16, input_shape=(1,)))
model.add(Activation('relu'))
model.add(Dense(nb_actions))
model.add(Activation('linear'))
memory = EpisodeParameterMemory(limit=1000, window_length=1)
dqn = CEMAgent(model=model, nb_actions=nb_actions, memory=memory)
dqn.compile()
dqn.fit(env, nb_steps=2000, visualize=False, verbose=1)
h = dqn.test(env, nb_episodes=20, visualize=False)
assert_allclose(np.mean(h.history['episode_reward']), 3.)
def test_cem():
env = TwoRoundDeterministicRewardEnv()
np.random.seed(123)
env.seed(123)
random.seed(123)
nb_actions = env.action_space.n
# Next, we build a very simple model.
model = Sequential()
model.add(Dense(16, input_shape=(1, )))
model.add(Activation('relu'))
model.add(Dense(nb_actions))
model.add(Activation('linear'))
memory = EpisodeParameterMemory(limit=1000, window_length=1)
dqn = CEMAgent(model=model, nb_actions=nb_actions, memory=memory)
dqn.compile()
dqn.fit(env, nb_steps=2000, visualize=False, verbose=1)
h = dqn.test(env, nb_episodes=20, visualize=False)
assert_allclose(np.mean(h.history['episode_reward']), 3.)
#model.add(Dense(1))
#model.add(Activation('relu'))
#model.add(Dense(nb_actions))
#model.add(Activation('softmax'))
print(model.summary())
# Finally, we configure and compile our agent. You can use every built-in Keras optimizer and
# even the metrics!
memory = EpisodeParameterMemory(limit=1000, window_length=1)
cem = CEMAgent(model=model,
nb_actions=nb_actions,
memory=memory,
batch_size=1,
nb_steps_warmup=10,
train_interval=10,
elite_frac=0.5)
cem.compile()
# Okay, now it's time to learn something! We visualize the training here for show, but this
# slows down training quite a lot. You can always safely abort the training prematurely using
# Ctrl + C.
cem.fit(env, nb_steps=100000, visualize=False, verbose=2)
# After training is done, we save the best weights.
cem.save_weights('cem_{}_params.h5f'.format(ENV_NAME), overwrite=True)
# Finally, evaluate our algorithm for 5 episodes.
cem.test(env, nb_episodes=10)