def run_experiment(variant, reporter):
env = get_environment('gym', 'MultiGoal', 'Default', {
'actuation_cost_coeff': 1,
'distance_cost_coeff': 0.1,
'goal_reward': 1,
'init_sigma': 0.1,
})
pool = SimpleReplayPool(
observation_space=env.observation_space,
action_space=env.action_space,
max_size=1e6)
sampler = SimpleSampler(
max_path_length=30, min_pool_size=100, batch_size=64)
Qs = get_Q_function_from_variant(variant, env)
policy = get_policy_from_variant(variant, env, Qs)
plotter = QFPolicyPlotter(
Q=Qs[0],
policy=policy,
obs_lst=np.array(((-2.5, 0.0),
(0.0, 0.0),
(2.5, 2.5),
(-2.5, -2.5))),
default_action=(np.nan, np.nan),
n_samples=100)
algorithm = SAC(
sampler=sampler,
reparameterize=True,
epoch_length=100,
n_epochs=1000,
n_train_repeat=1,
eval_render_mode=None,
eval_n_episodes=10,
eval_deterministic=False,
env=env,
policy=policy,
initial_exploration_policy=None,
pool=pool,
Qs=Qs,
plotter=plotter,
lr=3e-4,
target_entropy=-2.0,
discount=0.99,
tau=1e-4,
save_full_state=True,
)
initialize_tf_variables(algorithm._session, only_uninitialized=True)
for train_result in algorithm.train():
reporter(**train_result)
def run_experiment(variant, reporter):
training_environment = (get_environment(
'gym', 'MultiGoal', 'Default-v0', {
'actuation_cost_coeff': 30,
'distance_cost_coeff': 1,
'goal_reward': 10,
'init_sigma': 0.1,
}))
evaluation_environment = training_environment.copy()
pool = SimpleReplayPool(environment=training_environment, max_size=1e6)
sampler = SimpleSampler(max_path_length=30)
variant['Q_params']['config'].update({
'input_shapes': (
training_environment.observation_shape,
training_environment.action_shape,
)
})
Qs = value_functions.get(variant['Q_params'])
variant['policy_params']['config'].update({
'action_range': (training_environment.action_space.low,
training_environment.action_space.high),
'input_shapes':
training_environment.observation_shape,
'output_shape':
training_environment.action_shape,
})
policy = policies.get(variant['policy_params'])
plotter = QFPolicyPlotter(Q=Qs[0],
policy=policy,
obs_lst=np.array(((-2.5, 0.0), (0.0, 0.0),
(2.5, 2.5), (-2.5, -2.5))),
default_action=(np.nan, np.nan),
n_samples=100)
variant['algorithm_params']['config'].update({
'training_environment': training_environment,
'evaluation_environment': evaluation_environment,
'policy': policy,
'Qs': Qs,
'pool': pool,
'sampler': sampler,
'min_pool_size': 100,
'batch_size': 64,
'plotter': plotter,
})
algorithm = algorithms.get(variant['algorithm_params'])
for train_result in algorithm.train():
reporter(**train_result)
def run_experiment(variant, reporter):
training_environment = (
get_environment('gym', 'MultiGoal', 'Default-v0', {
'actuation_cost_coeff': 30,
'distance_cost_coeff': 1,
'goal_reward': 10,
'init_sigma': 0.1,
}))
evaluation_environment = training_environment.copy()
pool = SimpleReplayPool(
environment=training_environment,
max_size=1e6)
sampler = SimpleSampler(max_path_length=30)
Qs = get_Q_function_from_variant(variant, training_environment)
policy = get_policy_from_variant(variant, training_environment)
plotter = QFPolicyPlotter(
Q=Qs[0],
policy=policy,
obs_lst=np.array(((-2.5, 0.0),
(0.0, 0.0),
(2.5, 2.5),
(-2.5, -2.5))),
default_action=(np.nan, np.nan),
n_samples=100)
algorithm = get_algorithm_from_variant(
variant=variant,
training_environment=training_environment,
evaluation_environment=evaluation_environment,
policy=policy,
Qs=Qs,
pool=pool,
sampler=sampler,
min_pool_size=100,
batch_size=46,
plotter=plotter,
)
initialize_tf_variables(algorithm._session, only_uninitialized=True)
for train_result in algorithm.train():
reporter(**train_result)