from rl_coach.agents.ppo_agent import PPOAgentParameters
from rl_coach.architectures.layers import Dense
from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters, DistributedCoachSynchronizationType
from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps
from rl_coach.environments.environment import SingleLevelSelection
from rl_coach.environments.gym_environment import GymVectorEnvironment, mujoco_v2
from rl_coach.filters.filter import InputFilter
from rl_coach.filters.observation.observation_normalization_filter import ObservationNormalizationFilter
from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager
from rl_coach.graph_managers.graph_manager import ScheduleParameters

####################
# Graph Scheduling #
####################
schedule_params = ScheduleParameters()
schedule_params.improve_steps = TrainingSteps(1e10)
schedule_params.steps_between_evaluation_periods = EnvironmentSteps(4000)
schedule_params.evaluation_steps = EnvironmentEpisodes(1)
schedule_params.heatup_steps = EnvironmentSteps(0)

#########
# Agent #
#########
agent_params = PPOAgentParameters()
agent_params.network_wrappers['actor'].learning_rate = 5e-5
agent_params.network_wrappers['critic'].learning_rate = 5e-5

agent_params.network_wrappers['actor'].input_embedders_parameters['observation'].scheme = [Dense(64)]
agent_params.network_wrappers['actor'].middleware_parameters.scheme = [Dense(64)]
agent_params.network_wrappers['critic'].input_embedders_parameters['observation'].scheme = [Dense(64)]
agent_params.network_wrappers['critic'].middleware_parameters.scheme = [Dense(64)]

agent_params.input_filter = InputFilter()
agent_params.input_filter.add_observation_filter('observation', 'normalize', ObservationNormalizationFilter())

agent_params.algorithm.initial_kl_coefficient = 0.2
agent_params.algorithm.gae_lambda = 1.0

# Distributed Coach synchronization type.
agent_params.algorithm.distributed_coach_synchronization_type = DistributedCoachSynchronizationType.SYNC

###############
# Environment #
###############
env_params = GymVectorEnvironment(level=SingleLevelSelection(mujoco_v2))


########
# Test #
########
preset_validation_params = PresetValidationParameters()
preset_validation_params.test = True
preset_validation_params.min_reward_threshold = 400
preset_validation_params.max_episodes_to_achieve_reward = 3000
preset_validation_params.reward_test_level = 'inverted_pendulum'
preset_validation_params.trace_test_levels = ['inverted_pendulum', 'hopper']

graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params,
                                    schedule_params=schedule_params, vis_params=VisualizationParameters(),
                                    preset_validation_params=preset_validation_params)

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agent_params.input_filter.add_observation_filter('observation', 'normalize',
    ObservationNormalizationFilter())
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schedule_params = ScheduleParameters()
schedule_params.improve_steps = TrainingSteps(10000000000.0)
schedule_params.steps_between_evaluation_periods = EnvironmentSteps(4000)
schedule_params.evaluation_steps = EnvironmentEpisodes(1)
schedule_params.heatup_steps = EnvironmentSteps(0)
agent_params = PPOAgentParameters()
agent_params.network_wrappers['actor'].learning_rate = 5e-05
agent_params.network_wrappers['critic'].learning_rate = 5e-05
agent_params.network_wrappers['actor'].input_embedders_parameters['observation'
    ].scheme = [Dense(64)]
agent_params.network_wrappers['actor'].middleware_parameters.scheme = [Dense
    (64)]
agent_params.network_wrappers['critic'].input_embedders_parameters[
    'observation'].scheme = [Dense(64)]
agent_params.network_wrappers['critic'].middleware_parameters.scheme = [Dense
    (64)]
agent_params.input_filter = InputFilter()
agent_params.input_filter.add_observation_filter('observation', 'normalize',
    ObservationNormalizationFilter())
agent_params.algorithm.initial_kl_coefficient = 0.2
agent_params.algorithm.gae_lambda = 1.0
agent_params.algorithm.distributed_coach_synchronization_type = (
    DistributedCoachSynchronizationType.SYNC)
env_params = GymVectorEnvironment(level=SingleLevelSelection(mujoco_v2))
preset_validation_params = PresetValidationParameters()
preset_validation_params.test = True
preset_validation_params.min_reward_threshold = 400
preset_validation_params.max_episodes_to_achieve_reward = 3000
preset_validation_params.reward_test_level = 'inverted_pendulum'
preset_validation_params.trace_test_levels = ['inverted_pendulum', 'hopper']
graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=
    env_params, schedule_params=schedule_params, vis_params=
    VisualizationParameters(), preset_validation_params=
    preset_validation_params)

from rl_coach.agents.ppo_agent import PPOAgentParameters
from rl_coach.architectures.layers import Dense
from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters, DistributedCoachSynchronizationType
from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps
from rl_coach.environments.environment import SingleLevelSelection
from rl_coach.environments.gym_environment import GymVectorEnvironment, mujoco_v2
from rl_coach.filters.filter import InputFilter
from rl_coach.filters.observation.observation_normalization_filter import ObservationNormalizationFilter
from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager
from rl_coach.graph_managers.graph_manager import ScheduleParameters
schedule_params = ScheduleParameters()
schedule_params.improve_steps = TrainingSteps(10000000000.0)
schedule_params.steps_between_evaluation_periods = EnvironmentSteps(4000)
schedule_params.evaluation_steps = EnvironmentEpisodes(1)
schedule_params.heatup_steps = EnvironmentSteps(0)
agent_params = PPOAgentParameters()
agent_params.network_wrappers['actor'].learning_rate = 5e-05
agent_params.network_wrappers['critic'].learning_rate = 5e-05
agent_params.network_wrappers['actor'].input_embedders_parameters['observation'
    ].scheme = [Dense(64)]
agent_params.network_wrappers['actor'].middleware_parameters.scheme = [Dense
    (64)]
agent_params.network_wrappers['critic'].input_embedders_parameters[
    'observation'].scheme = [Dense(64)]
agent_params.network_wrappers['critic'].middleware_parameters.scheme = [Dense
    (64)]
agent_params.input_filter = InputFilter()
agent_params.input_filter.add_observation_filter('observation', 'normalize',
    ObservationNormalizationFilter())
agent_params.algorithm.initial_kl_coefficient = 0.2
agent_params.algorithm.gae_lambda = 1.0
agent_params.algorithm.distributed_coach_synchronization_type = (
    DistributedCoachSynchronizationType.SYNC)
env_params = GymVectorEnvironment(level=SingleLevelSelection(mujoco_v2))
preset_validation_params = PresetValidationParameters()
preset_validation_params.test = True
preset_validation_params.min_reward_threshold = 400
preset_validation_params.max_episodes_to_achieve_reward = 3000
preset_validation_params.reward_test_level = 'inverted_pendulum'
preset_validation_params.trace_test_levels = ['inverted_pendulum', 'hopper']
graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=
    env_params, schedule_params=schedule_params, vis_params=
    VisualizationParameters(), preset_validation_params=
    preset_validation_params)

from rl_coach.agents.ppo_agent import PPOAgentParameters
from rl_coach.architectures.layers import Dense
from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters, DistributedCoachSynchronizationType
from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps
from rl_coach.environments.environment import SingleLevelSelection
from rl_coach.environments.gym_environment import GymVectorEnvironment, mujoco_v2
from rl_coach.filters.filter import InputFilter
from rl_coach.filters.observation.observation_normalization_filter import ObservationNormalizationFilter
from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager
from rl_coach.graph_managers.graph_manager import ScheduleParameters

####################
# Graph Scheduling #
####################
schedule_params = ScheduleParameters()
schedule_params.improve_steps = TrainingSteps(1e10)
schedule_params.steps_between_evaluation_periods = EnvironmentSteps(4000)
schedule_params.evaluation_steps = EnvironmentEpisodes(1)
schedule_params.heatup_steps = EnvironmentSteps(0)

#########
# Agent #
#########
agent_params = PPOAgentParameters()
agent_params.network_wrappers['actor'].learning_rate = 5e-5
agent_params.network_wrappers['critic'].learning_rate = 5e-5

agent_params.network_wrappers['actor'].input_embedders_parameters['observation'].scheme = [Dense(64)]
agent_params.network_wrappers['actor'].middleware_parameters.scheme = [Dense(64)]
agent_params.network_wrappers['critic'].input_embedders_parameters['observation'].scheme = [Dense(64)]
agent_params.network_wrappers['critic'].middleware_parameters.scheme = [Dense(64)]

agent_params.input_filter = InputFilter()
agent_params.input_filter.add_observation_filter('observation', 'normalize', ObservationNormalizationFilter())

agent_params.algorithm.initial_kl_coefficient = 0.2
agent_params.algorithm.gae_lambda = 1.0

# Distributed Coach synchronization type.
agent_params.algorithm.distributed_coach_synchronization_type = DistributedCoachSynchronizationType.SYNC

###############
# Environment #
###############
env_params = GymVectorEnvironment(level=SingleLevelSelection(mujoco_v2))


########
# Test #
########
preset_validation_params = PresetValidationParameters()
preset_validation_params.test = True
preset_validation_params.min_reward_threshold = 400
preset_validation_params.max_episodes_to_achieve_reward = 3000
preset_validation_params.reward_test_level = 'inverted_pendulum'
preset_validation_params.trace_test_levels = ['inverted_pendulum', 'hopper']

graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params,
                                    schedule_params=schedule_params, vis_params=VisualizationParameters(),
                                    preset_validation_params=preset_validation_params)
