"""
A simple example to show how to build up an experiment with ddpg training and testing on Pendulum-v0
"""
from baconian.core.core import EnvSpec
from baconian.envs.gym_env import make
from baconian.algo.value_func.mlp_q_value import MLPQValueFunction
from baconian.algo.ddpg import DDPG
from baconian.algo.policy import DeterministicMLPPolicy
from baconian.core.agent import Agent
from baconian.algo.misc import EpsilonGreedy
from baconian.core.experiment import Experiment
from baconian.core.flow.train_test_flow import create_train_test_flow
from baconian.config.global_config import GlobalConfig
from baconian.core.status import get_global_status_collect
from baconian.common.schedules import PeriodicalEventSchedule
import baconian.common.log_data_loader as loader
from pathlib import Path


def task_fn():
    env = make('Pendulum-v0')
    name = 'demo_exp'
    env_spec = EnvSpec(obs_space=env.observation_space,
                       action_space=env.action_space)

    mlp_q = MLPQValueFunction(env_spec=env_spec,
                              name_scope=name + '_mlp_q',
                              name=name + '_mlp_q',
                              mlp_config=[
                                  {
                                      "ACT": "RELU",
                                      "B_INIT_VALUE": 0.0,
                                      "NAME": "1",
                                      "N_UNITS": 16,
                                      "TYPE": "DENSE",
                                      "W_NORMAL_STDDEV": 0.03
                                  },
                                  {
                                      "ACT": "LINEAR",
                                      "B_INIT_VALUE": 0.0,
                                      "NAME": "OUPTUT",
                                      "N_UNITS": 1,
                                      "TYPE": "DENSE",
                                      "W_NORMAL_STDDEV": 0.03
                                  }
                              ])
    policy = DeterministicMLPPolicy(env_spec=env_spec,
                                    name_scope=name + '_mlp_policy',
                                    name=name + '_mlp_policy',
                                    mlp_config=[
                                        {
                                            "ACT": "RELU",
                                            "B_INIT_VALUE": 0.0,
                                            "NAME": "1",
                                            "N_UNITS": 16,
                                            "TYPE": "DENSE",
                                            "W_NORMAL_STDDEV": 0.03
                                        },
                                        {
                                            "ACT": "LINEAR",
                                            "B_INIT_VALUE": 0.0,
                                            "NAME": "OUPTUT",
                                            "N_UNITS": env_spec.flat_action_dim,
                                            "TYPE": "DENSE",
                                            "W_NORMAL_STDDEV": 0.03
                                        }
                                    ],
                                    reuse=False)

    ddpg = DDPG(
        env_spec=env_spec,
        config_or_config_dict={
            "REPLAY_BUFFER_SIZE": 10000,
            "GAMMA": 0.999,
            "CRITIC_LEARNING_RATE": 0.001,
            "ACTOR_LEARNING_RATE": 0.001,
            "DECAY": 0.5,
            "BATCH_SIZE": 50,
            "TRAIN_ITERATION": 1,
            "critic_clip_norm": 0.1,
            "actor_clip_norm": 0.1,
        },
        value_func=mlp_q,
        policy=policy,
        name=name + '_ddpg',
        replay_buffer=None
    )
    agent = Agent(env=env, env_spec=env_spec,
                  algo=ddpg,
                  algo_saving_scheduler=PeriodicalEventSchedule(
                      t_fn=lambda: get_global_status_collect()('TOTAL_AGENT_TRAIN_SAMPLE_COUNT'),
                      trigger_every_step=20,
                      after_t=10),
                  name=name + '_agent',
                  exploration_strategy=EpsilonGreedy(action_space=env_spec.action_space,
                                                     init_random_prob=0.5))

    flow = create_train_test_flow(
        test_every_sample_count=10,
        train_every_sample_count=10,
        start_test_after_sample_count=5,
        start_train_after_sample_count=5,
        train_func_and_args=(agent.train, (), dict()),
        test_func_and_args=(agent.test, (), dict(sample_count=1)),
        sample_func_and_args=(agent.sample, (), dict(sample_count=100,
                                                     env=agent.env,
                                                     store_flag=True))
    )

    experiment = Experiment(
        tuner=None,
        env=env,
        agent=agent,
        flow=flow,
        name=name
    )
    experiment.run()


from baconian.core.experiment_runner import *

GlobalConfig().set('DEFAULT_LOG_PATH', './log_path')
single_exp_runner(task_fn, del_if_log_path_existed=True)

"""
A simple example to show how to build up an experiment with ddpg training and testing on Pendulum-v0
"""
from baconian.core.core import EnvSpec
from baconian.envs.gym_env import make
from baconian.algo.value_func.mlp_q_value import MLPQValueFunction
from baconian.algo.ddpg import DDPG
from baconian.algo.policy import DeterministicMLPPolicy
from baconian.core.agent import Agent
from baconian.algo.misc import EpsilonGreedy
from baconian.core.experiment import Experiment
from baconian.core.flow.train_test_flow import create_train_test_flow
from baconian.config.global_config import GlobalConfig
from baconian.core.status import get_global_status_collect
from baconian.common.schedules import PeriodicalEventSchedule
import baconian.common.log_data_loader as loader
from pathlib import Path


def task_fn():
    env = make('Pendulum-v0')
    name = 'demo_exp'
    env_spec = EnvSpec(obs_space=env.observation_space,
                       action_space=env.action_space)

    mlp_q = MLPQValueFunction(env_spec=env_spec,
                              name_scope=name + '_mlp_q',
                              name=name + '_mlp_q',
                              mlp_config=[
                                  {
                                      "ACT": "RELU",
                                      "B_INIT_VALUE": 0.0,
                                      "NAME": "1",
                                      "N_UNITS": 16,
                                      "TYPE": "DENSE",
                                      "W_NORMAL_STDDEV": 0.03
                                  },
                                  {
                                      "ACT": "LINEAR",
                                      "B_INIT_VALUE": 0.0,
                                      "NAME": "OUPTUT",
                                      "N_UNITS": 1,
                                      "TYPE": "DENSE",
                                      "W_NORMAL_STDDEV": 0.03
                                  }
                              ])
    policy = DeterministicMLPPolicy(env_spec=env_spec,
                                    name_scope=name + '_mlp_policy',
                                    name=name + '_mlp_policy',
                                    mlp_config=[
                                        {
                                            "ACT": "RELU",
                                            "B_INIT_VALUE": 0.0,
                                            "NAME": "1",
                                            "N_UNITS": 16,
                                            "TYPE": "DENSE",
                                            "W_NORMAL_STDDEV": 0.03
                                        },
                                        {
                                            "ACT": "LINEAR",
                                            "B_INIT_VALUE": 0.0,
                                            "NAME": "OUPTUT",
                                            "N_UNITS": env_spec.flat_action_dim,
                                            "TYPE": "DENSE",
                                            "W_NORMAL_STDDEV": 0.03
                                        }
                                    ],
                                    reuse=False)

    ddpg = DDPG(
        env_spec=env_spec,
        config_or_config_dict={
            "REPLAY_BUFFER_SIZE": 10000,
            "GAMMA": 0.999,
            "CRITIC_LEARNING_RATE": 0.001,
            "ACTOR_LEARNING_RATE": 0.001,
            "DECAY": 0.5,
            "BATCH_SIZE": 50,
            "TRAIN_ITERATION": 1,
            "critic_clip_norm": 0.1,
            "actor_clip_norm": 0.1,
        },
        value_func=mlp_q,
        policy=policy,
        name=name + '_ddpg',
        replay_buffer=None
    )
    agent = Agent(env=env, env_spec=env_spec,
                  algo=ddpg,
                  algo_saving_scheduler=PeriodicalEventSchedule(
                      t_fn=lambda: get_global_status_collect()('TOTAL_AGENT_TRAIN_SAMPLE_COUNT'),
                      trigger_every_step=20,
                      after_t=10),
                  name=name + '_agent',
                  exploration_strategy=EpsilonGreedy(action_space=env_spec.action_space,
                                                     init_random_prob=0.5))

    flow = create_train_test_flow(
        test_every_sample_count=10,
        train_every_sample_count=10,
        start_test_after_sample_count=5,
        start_train_after_sample_count=5,
        train_func_and_args=(agent.train, (), dict()),
        test_func_and_args=(agent.test, (), dict(sample_count=1)),
        sample_func_and_args=(agent.sample, (), dict(sample_count=100,
                                                     env=agent.env,
                                                     store_flag=True))
    )

    experiment = Experiment(
        tuner=None,
        env=env,
        agent=agent,
        flow=flow,
        name=name
    )
    experiment.run()


from baconian.core.experiment_runner import *

GlobalConfig().set('DEFAULT_LOG_PATH', './log_path')
single_exp_runner(task_fn, del_if_log_path_existed=True)

A simple example to show how to build up an experiment with ddpg training and testing on Pendulum-v0