"""
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)