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 _build(self):
variant = copy.deepcopy(self._variant)
environment_params = variant['environment_params']
training_environment = self.training_environment = (
get_roboverse_env_from_params(environment_params['training']))
evaluation_environment = self.evaluation_environment = (
get_roboverse_env_from_params(environment_params['evaluation'])
if 'evaluation' in environment_params
else training_environment)
from collections import OrderedDict
changed_obs_shape = OrderedDict()
changed_obs_shape['image'] = training_environment.observation_shape['image']
variant['Q_params']['config'].update({
'input_shapes': (
#training_environment.observation_shape,
changed_obs_shape,
training_environment.action_shape),
})
Qs = self.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': changed_obs_shape, #training_environment.observation_shape,
'output_shape': training_environment.action_shape,
})
policy = self.policy = policies.get(variant['policy_params'])
variant['replay_pool_params']['config'].update({
'environment': training_environment,
})
replay_pool = self.replay_pool = replay_pools.get(
variant['replay_pool_params'])
variant['sampler_params']['config'].update({
'environment': training_environment,
'policy': policy,
'pool': replay_pool,
})
sampler = self.sampler = samplers.get(variant['sampler_params'])
variant['algorithm_params']['config'].update({
'training_environment': training_environment,
'evaluation_environment': evaluation_environment,
'policy': policy,
'Qs': Qs,
'pool': replay_pool,
'sampler': sampler
})
self.algorithm = algorithms.get(variant['algorithm_params'])
self._built = True
def _build(self):
variant = copy.deepcopy(self._variant)
environment_params = variant['environment_params']
training_environment = self.training_environment = (
get_environment_from_params(environment_params['training']))
evaluation_environment = self.evaluation_environment = (
get_environment_from_params(environment_params['evaluation'])
if 'evaluation' in environment_params else training_environment)
variant['Q_params']['config'].update({
'input_shapes': (training_environment.observation_shape,
training_environment.action_shape),
})
Qs = self.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 = self.policy = policies.get(variant['policy_params'])
variant['replay_pool_params']['config'].update({
'environment':
training_environment,
})
replay_pool = self.replay_pool = replay_pools.get(
variant['replay_pool_params'])
variant['sampler_params']['config'].update({
'environment': training_environment,
'policy': policy,
'pool': replay_pool,
})
sampler = self.sampler = samplers.get(variant['sampler_params'])
self.sampler.seed = variant['run_params']['seed']
print(sampler.seed, self.sampler.seed)
variant['algorithm_params']['config'].update({
'training_environment': training_environment,
'evaluation_environment': evaluation_environment,
'policy': policy,
'Qs': Qs,
'pool': replay_pool,
'sampler': sampler
})
self.algorithm = algorithms.get(variant['algorithm_params'])
self._built = True
def load_policy(checkpoint_dir, variant, environment):
policy_params = variant['policy_params'].copy()
policy_params['config'] = {
**policy_params['config'],
'action_range':
(environment.action_space.low, environment.action_space.high),
'input_shapes':
environment.observation_shape,
'output_shape':
environment.action_shape,
}
policy = policies.get(policy_params)
policy_save_path = ExperimentRunner._policy_save_path(checkpoint_dir)
status = policy.load_weights(policy_save_path)
status.assert_consumed().run_restore_ops()
return policy
def _build(self):
'''
variant['something params']是关于 something 的创建参数，
其中又包含 variant['something params']['class_name']
和 variant['something params']['config']
两项。
用这两项可以创建一个对象实例
'''
variant = copy.deepcopy(self._variant)
environment_params = variant['environment_params']
training_environment = self.training_environment = (
get_environment_from_params(environment_params['training']))
evaluation_environment = self.evaluation_environment = (
get_environment_from_params(environment_params['evaluation'])
if 'evaluation' in environment_params else training_environment)
variant['Q_params']['config'].update({
'input_shapes': (training_environment.observation_shape,
training_environment.action_shape),
})
# 根据配置获取一个函数(包含神经网络)的实例
Qs = self.Qs = tree.flatten(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 = self.policy = policies.get(variant['policy_params'])
variant['replay_pool_params']['config'].update({
'environment':
training_environment,
})
# 参考 value_functions.get， 根据配置获取实例
replay_pool = self.replay_pool = replay_pools.get(
variant['replay_pool_params'])
# 用 variant 中的下层配置创建下层对象，并用下层对象给上层配置(config)赋值
variant['sampler_params']['config'].update({
'environment': training_environment,
'policy': policy,
'pool': replay_pool,
})
# 用上层配置创建上层对象
sampler = self.sampler = samplers.get(variant['sampler_params'])
# 用 variant 中的下层配置创建下层对象，并用下层对象给上层配置赋值
variant['algorithm_params']['config'].update({
'training_environment': training_environment,
'evaluation_environment': evaluation_environment,
'policy': policy,
'Qs': Qs,
'pool': replay_pool,
'sampler': sampler
})
# 用上层配置创建上层对象，创建 RL 算法，包含所有运算模块
self.algorithm = algorithms.get(variant['algorithm_params'])
self._built = True
def _build(self):
variant = copy.deepcopy(self._variant)
environment_params = variant['environment_params']
training_environment = self.training_environment = (
get_environment_from_params(environment_params['training']))
evaluation_environment = self.evaluation_environment = (
get_environment_from_params(environment_params['evaluation'])
if 'evaluation' in environment_params
else training_environment)
variant['Q_params']['config'].update({
'input_shapes': (
training_environment.observation_shape,
training_environment.action_shape),
})
Qs = self.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 = self.policy = policies.get(variant['policy_params'])
variant['replay_pool_params']['config'].update({
'environment': training_environment,
})
replay_pool = self.replay_pool = replay_pools.get(
variant['replay_pool_params'])
variant['sampler_params']['config'].update({
'environment': training_environment,
'policy': policy,
'pool': replay_pool,
})
sampler = self.sampler = samplers.get(variant['sampler_params'])
set_random_seed(variant['run_params']['seed'])
save_path = os.path.join(os.path.dirname(__file__),"..","..", "results", f"logs",f"sac", f"HalfCheetahBulletEnv-v0_{variant['run_params']['seed']}")
print("this is the save path: " + save_path)
os.makedirs(save_path, exist_ok=True)
# create wrapped environment
eval_env_wrapped = TimeLimit(evaluation_environment, 1000)
eval_callback = EvalCallback(
eval_env_wrapped,
callback_on_new_best=None,
best_model_save_path=None,
n_eval_episodes=10,
log_path=save_path,
eval_freq=10000, # TODO change hardcoded value
deterministic=True,
verbose=1,
)
eval_callback.init_callback(policy)
sampler.set_callback(eval_callback)
variant['algorithm_params']['config'].update({
'training_environment': training_environment,
'evaluation_environment': evaluation_environment,
'policy': policy,
'Qs': Qs,
'pool': replay_pool,
'sampler': sampler
})
self.algorithm = algorithms.get(variant['algorithm_params'])
self._built = True
def main(variant_in):
variant = copy.deepcopy(variant_in)
environment_params = variant['environment_params']
training_environment = get_environment_from_params(environment_params['training'])
evaluation_environment = (
get_environment_from_params(environment_params['evaluation'])
if 'evaluation' in environment_params else training_environment
)
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'])
variant['replay_pool_params']['config'].update({
'environment': training_environment,
})
replay_pool = replay_pools.get(variant['replay_pool_params'])
variant['sampler_params']['config'].update({
'environment': training_environment,
'policy': policy,
'pool': replay_pool,
})
sampler = samplers.get(variant['sampler_params'])
variant['algorithm_params']['config'].update({
'training_environment': training_environment,
'evaluation_environment': evaluation_environment,
'policy': policy,
'Qs': Qs,
'pool': replay_pool,
'sampler': sampler
})
algorithm = algorithms.get(variant['algorithm_params'])
print("Initialization finished")
train_generator = None
# it will iterate through the number of epochs 'n_epochs'
# during epoch:
# it will sample 'epoch_length' number of times (reset is not counted) to the pool
# also, it will train each step, if there are more samples than 'min_pool_size' in the replay pool
for i in count():
if train_generator is None:
train_generator = algorithm.train()
diagnostics = next(train_generator)
# it should be before printing to prevent a double print the last epoch
try:
if diagnostics['done']:
break
except KeyError:
pass
evalu_reward = diagnostics["evaluation"]["episode-reward-mean"]
print(f"Evaluation: reward mean is {evalu_reward}")
# train_reward = diagnostics["training"]["episode-reward-mean"]
# print(f"Training: reward mean is {train_reward}")
print("Finish")
return policy