def __init__(
self,
gym_env_name: t.Text,
model_shape: t.Iterable[int] = (20, 20, 20),
report_every_num_of_episodes: int = 100,
):
"""Ctor.
Default implementations are provided for all objects. They can be changed
by directly setting the public properties after the creation.
Args:
gym_env_name: name of the gym environment, like "LunarLander-v2".
model_shape: a list of number of nodes per hidden layer.
report_every_num_of_episodes: do progress report every this number of
episodes.
"""
self._gym_env_name = gym_env_name
self._model_shape = tuple(model_shape)
self.env = environment_impl.GymEnvironment(gym.make(gym_env_name))
self.qfunc = qfunc_impl.DDQN(
model_pair=(qfunc_impl.CreateModel(
state_shape=self.env.GetStateShape(),
action_space_size=self.env.GetActionSpaceSize(),
hidden_layer_sizes=model_shape),
qfunc_impl.CreateModel(
state_shape=self.env.GetStateShape(),
action_space_size=self.env.GetActionSpaceSize(),
hidden_layer_sizes=model_shape)),
training_batch_size=DEFAULT_BATCH_SIZE,
discount_factor=0.99,
)
logging.printf('Using qfunc implementation: %s',
string.GetClassName(self.qfunc))
self.policy = policy_impl.GreedyPolicyWithDecreasingRandomness(
initial_epsilon=1.0,
final_epsilon=0.1,
decay_by_half_after_num_of_episodes=500)
logging.printf('Using policy implementation: %s',
string.GetClassName(self.policy))
self.runner = runner_impl.ExperienceReplayRunner(
experience_capacity=100000,
experience_sample_batch_size=DEFAULT_BATCH_SIZE)
logging.printf('Using runner implementation: %s',
string.GetClassName(self.runner))
self._progress_tracer = runner_extension_impl.ProgressTracer(
report_every_num_of_episodes=report_every_num_of_episodes)
self._model_saver = runner_extension_impl.ModelSaver(
self._GetModelWeightsFilepath())
def main(_):
batch_size = 64 # used in qfunc and runner.
env = environment_impl.GymEnvironment(gym.make('MountainCar-v0'))
env.SetGymEnvMaxEpisodeSteps(400)
qfunc = qfunc_impl.DQN(
model=qfunc_impl.CreateModel(
state_shape=env.GetStateShape(),
action_space_size=env.GetActionSpaceSize(),
hidden_layer_sizes=(64, )),
training_batch_size=batch_size,
discount_factor=0.99,
)
qfunc.Load('saved_models/mountaincar_shape_64_rmsprop_gamma_099.weights')
policy = policy_impl.GreedyPolicy()
runner = runner_impl.NoOpRunner()
env.TurnOnRendering(should_render=True, fps=24)
logging.ENV.debug_verbosity = 9
env.StartRecording(video_filename='mountaincar_demo.mp4')
# First 5 runs with random actions:
rand_qfunc = qfunc_impl.RandomQFunction(env.GetActionSpaceSize())
runner.Run(env=env, brain=rand_qfunc, policy=policy, num_of_episodes=5)
# Then 10 runs with trained qfunc:
runner.Run(env=env, brain=qfunc, policy=policy, num_of_episodes=10)
env.StopRecording()
def main(_):
batch_size = 64 # used in qfunc and runner.
env = environment_impl.GymEnvironment(gym.make('CartPole-v0'))
qfunc = qfunc_impl.DQN(
model=qfunc_impl.CreateModel(
state_shape=env.GetStateShape(),
action_space_size=env.GetActionSpaceSize(),
hidden_layer_sizes=(20, 20, 20)),
training_batch_size=batch_size,
discount_factor=0.99,
)
runner = runner_impl.ExperienceReplayRunner(
experience_capacity=100000, experience_sample_batch_size=batch_size)
# Train 500 episodes.
logging.ENV.debug_verbosity = 3
policy = policy_impl.GreedyPolicyWithRandomness(epsilon=0.1)
runner.Run(env=env, brain=qfunc, policy=policy, num_of_episodes=500)
# Test for 100 episodes.
logging.ENV.debug_verbosity = 4
policy = policy_impl.GreedyPolicy()
runner.Run(env=env, brain=qfunc, policy=policy, num_of_episodes=100)
# Demo with video.
env.TurnOnRendering(should_render=True, fps=24)
# env.StartRecording(video_filename='demo.mp4') # uncomment to record video.
# First 5 runs with random actions:
runner.Run(env=env,
brain=qfunc_impl.RandomQFunction(env.GetActionSpaceSize()),
policy=policy,
num_of_episodes=5)
# Then 10 runs with trained qfunc:
runner.Run(env=env, brain=qfunc, policy=policy, num_of_episodes=10)
def test_saveLoad(self):
tmp_file = '/tmp/DDQNTest_savedata.tmp'
self.qfunc._SetValues(self.states, self.values)
self.qfunc.Save(tmp_file)
qfunc = qfunc_impl.DDQN(model_pair=(qfunc_impl.CreateModel(
state_shape=(3, ),
action_space_size=2,
hidden_layer_sizes=(3, ),
),
qfunc_impl.CreateModel(
state_shape=(3, ),
action_space_size=2,
hidden_layer_sizes=(3, ),
)), )
qfunc.Load(tmp_file)
numpy_util.TestUtil.AssertModelWeightsEqual(qfunc._q1,
self.qfunc._model)
numpy_util.TestUtil.AssertModelWeightsEqual(qfunc._q2,
self.qfunc._model)
def setUp(self) -> None:
# State space size is 3; Action space size is 2.
self.qfunc = qfunc_impl.DDQN(
model_pair=(qfunc_impl.CreateModel(
state_shape=(3, ),
action_space_size=2,
hidden_layer_sizes=(3, ),
),
qfunc_impl.CreateModel(
state_shape=(3, ),
action_space_size=2,
hidden_layer_sizes=(3, ),
)),
discount_factor=0.9,
)
self.states = numpy.array([
[1, 2, 3],
[4, 5, 6],
])
self.values = numpy.array([
[0.5, 0.5],
[0.3, 0.7],
])
def _RunEnv(gym_env):
env = environment_impl.GymEnvironment(gym_env)
env.SetGymEnvMaxEpisodeSteps(10)
qfunc = qfunc_impl.DQN(model=qfunc_impl.CreateModel(
state_shape=env.GetStateShape(),
action_space_size=env.GetActionSpaceSize(),
hidden_layer_sizes=(4, ),
))
env.Reset()
policy = policy_impl.GreedyPolicyWithRandomness(epsilon=1.0)
runner_impl.SimpleRunner().Run(env=env,
qfunc=qfunc,
policy=policy,
num_of_episodes=10)
def main(_):
batch_size = 64 # used in qfunc and runner.
env = environment_impl.GymEnvironment(gym.make('Acrobot-v1'))
qfunc = qfunc_impl.DQN(
model=qfunc_impl.CreateModel(
state_shape=env.GetStateShape(),
action_space_size=env.GetActionSpaceSize(),
hidden_layer_sizes=(20, 20, 20)),
training_batch_size=batch_size,
discount_factor=0.99,
)
qfunc.LoadModel(
'saved_models/acrobot_v1_shape_20-20-20_rmsprop_gamma_0.99.model')
policy = policy_impl.GreedyPolicy()
runner = runner_impl.NoOpRunner()
env.TurnOnRendering(should_render=True, fps=10)
logging.ENV.debug_verbosity = 9
runner.Run(env=env, qfunc=qfunc, policy=policy, num_of_episodes=10)
def setUp(self) -> None:
# State space size is 3; Action space size is 2.
self.qfunc = qfunc_impl.DQN_TargetNetwork(
model=qfunc_impl.CreateModel(
state_shape=(3, ),
action_space_size=2,
hidden_layer_sizes=(6, 4),
),
update_target_network_every_num_of_steps=2,
)
self.states = numpy.array([
[1, 2, 3],
[4, 5, 6],
])
self.values = numpy.array([
[0.5, 0.5],
[0.3, 0.7],
])
