def create_dyna_flow(self, agent, env):
flow = DynaFlow(
train_sample_count_func=lambda: get_global_status_collect()
('TOTAL_AGENT_TRAIN_SAMPLE_COUNT'),
config_or_config_dict={
"TEST_ALGO_EVERY_REAL_SAMPLE_COUNT": 10,
"TEST_DYNAMICS_EVERY_REAL_SAMPLE_COUNT": 10,
"TRAIN_ALGO_EVERY_REAL_SAMPLE_COUNT_FROM_REAL_ENV": 10,
"TRAIN_ALGO_EVERY_REAL_SAMPLE_COUNT_FROM_DYNAMICS_ENV": 10,
"TRAIN_DYNAMICS_EVERY_REAL_SAMPLE_COUNT": 10,
"START_TRAIN_ALGO_AFTER_SAMPLE_COUNT": 1,
"START_TRAIN_DYNAMICS_AFTER_SAMPLE_COUNT": 1,
"START_TEST_ALGO_AFTER_SAMPLE_COUNT": 1,
"START_TEST_DYNAMICS_AFTER_SAMPLE_COUNT": 1,
"WARM_UP_DYNAMICS_SAMPLES": 1
},
func_dict={
'train_algo': {
'func': agent.train,
'args': list(),
'kwargs': dict(state='state_agent_training')
},
'train_algo_from_synthesized_data': {
'func': agent.train,
'args': list(),
'kwargs': dict(state='state_agent_training')
},
'train_dynamics': {
'func': agent.train,
'args': list(),
'kwargs': dict(state='state_dynamics_training')
},
'test_algo': {
'func': agent.test,
'args': list(),
'kwargs': dict(sample_count=10)
},
'test_dynamics': {
'func': agent.algo.test_dynamics,
'args': list(),
'kwargs': dict(sample_count=10, env=env)
},
'sample_from_real_env': {
'func':
agent.sample,
'args':
list(),
'kwargs':
dict(sample_count=10,
env=agent.env,
in_which_status='TRAIN',
store_flag=True)
},
'sample_from_dynamics_env': {
'func':
agent.sample,
'args':
list(),
'kwargs':
dict(sample_count=10,
env=agent.algo.dynamics_env,
in_which_status='TRAIN',
store_flag=True)
}
})
return flow, locals()
def pendulum_task_fn():
GlobalConfig().set('DEFAULT_EXPERIMENT_END_POINT',
exp_config['DEFAULT_EXPERIMENT_END_POINT'])
env = make('Pendulum-v0')
name = 'benchmark'
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',
**exp_config['MLPQValueFunction'])
policy = DeterministicMLPPolicy(env_spec=env_spec,
name_scope=name + '_mlp_policy',
name=name + '_mlp_policy',
output_low=env_spec.action_space.low,
output_high=env_spec.action_space.high,
**exp_config['DeterministicMLPPolicy'],
reuse=False)
ddpg = DDPG(env_spec=env_spec,
policy=policy,
value_func=mlp_q,
name=name + '_ddpg',
**exp_config['DDPG'])
mlp_dyna = ContinuousMLPGlobalDynamicsModel(
env_spec=env_spec,
name_scope=name + '_mlp_dyna',
name=name + '_mlp_dyna',
output_low=env_spec.obs_space.low,
output_high=env_spec.obs_space.high,
**exp_config['DynamicsModel'])
algo = Dyna(env_spec=env_spec,
name=name + '_dyna_algo',
model_free_algo=ddpg,
dynamics_model=mlp_dyna,
config_or_config_dict=dict(dynamics_model_train_iter=10,
model_free_algo_train_iter=10))
algo.set_terminal_reward_function_for_dynamics_env(
terminal_func=FixedEpisodeLengthTerminalFunc(
max_step_length=env.unwrapped._max_episode_steps,
step_count_fn=algo.dynamics_env.total_step_count_fn),
reward_func=REWARD_FUNC_DICT['Pendulum-v0']())
agent = Agent(env=env,
env_spec=env_spec,
algo=algo,
exploration_strategy=None,
noise_adder=AgentActionNoiseWrapper(
noise=NormalActionNoise(),
noise_weight_scheduler=ConstantSchedule(value=0.3),
action_weight_scheduler=ConstantSchedule(value=1.0)),
name=name + '_agent')
flow = DynaFlow(
train_sample_count_func=lambda: get_global_status_collect()
('TOTAL_AGENT_TRAIN_SAMPLE_COUNT'),
config_or_config_dict=exp_config['DynaFlow'],
func_dict={
'train_algo': {
'func': agent.train,
'args': list(),
'kwargs': dict(state='state_agent_training')
},
'train_algo_from_synthesized_data': {
'func': agent.train,
'args': list(),
'kwargs': dict(state='state_agent_training', train_iter=1)
},
'train_dynamics': {
'func': agent.train,
'args': list(),
'kwargs': dict(state='state_dynamics_training')
},
'test_algo': {
'func': agent.test,
'args': list(),
'kwargs': dict(sample_count=1, sample_trajectory_flag=True)
},
'test_dynamics': {
'func': agent.algo.test_dynamics,
'args': list(),
'kwargs': dict(sample_count=10, env=env)
},
'sample_from_real_env': {
'func':
agent.sample,
'args':
list(),
'kwargs':
dict(sample_count=10,
env=agent.env,
in_which_status='TRAIN',
store_flag=True)
},
'sample_from_dynamics_env': {
'func':
agent.sample,
'args':
list(),
'kwargs':
dict(sample_count=50,
sample_type='transition',
env=agent.algo.dynamics_env,
in_which_status='TRAIN',
store_flag=False)
}
})
experiment = Experiment(tuner=None,
env=env,
agent=agent,
flow=flow,
name=name)
experiment.run()
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,
"Q_NET_L1_NORM_SCALE": 0.01,
"Q_NET_L2_NORM_SCALE": 0.01,
"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)
mlp_dyna = ContinuousMLPGlobalDynamicsModel(
env_spec=env_spec,
name_scope=name + '_mlp_dyna',
name=name + '_mlp_dyna',
output_low=env_spec.obs_space.low,
output_high=env_spec.obs_space.high,
learning_rate=0.01,
mlp_config=[{
"ACT": "RELU",
"B_INIT_VALUE": 0.0,
"NAME": "1",
"L1_NORM": 0.0,
"L2_NORM": 0.0,
"N_UNITS": 16,
"TYPE": "DENSE",
"W_NORMAL_STDDEV": 0.03
}, {
"ACT": "LINEAR",
"B_INIT_VALUE": 0.0,
"NAME": "OUPTUT",
"L1_NORM": 0.0,
"L2_NORM": 0.0,
"N_UNITS": env_spec.flat_obs_dim,
"TYPE": "DENSE",
"W_NORMAL_STDDEV": 0.03
}])
algo = Dyna(env_spec=env_spec,
name=name + '_dyna_algo',
model_free_algo=ddpg,
dynamics_model=mlp_dyna,
config_or_config_dict=dict(dynamics_model_train_iter=10,
model_free_algo_train_iter=10))
# For examples only, we use random reward function and terminal function with fixed episode length.
algo.set_terminal_reward_function_for_dynamics_env(
terminal_func=FixedEpisodeLengthTerminalFunc(
max_step_length=env.unwrapped._max_episode_steps,
step_count_fn=algo.dynamics_env.total_step_count_fn),
reward_func=RandomRewardFunc())
agent = Agent(
env=env,
env_spec=env_spec,
algo=algo,
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 = DynaFlow(
train_sample_count_func=lambda: get_global_status_collect()
('TOTAL_AGENT_TRAIN_SAMPLE_COUNT'),
config_or_config_dict={
"TRAIN_ALGO_EVERY_REAL_SAMPLE_COUNT_FROM_REAL_ENV": 10,
"TRAIN_ALGO_EVERY_REAL_SAMPLE_COUNT_FROM_DYNAMICS_ENV": 10,
"TEST_ALGO_EVERY_REAL_SAMPLE_COUNT": 10,
"TEST_DYNAMICS_EVERY_REAL_SAMPLE_COUNT": 10,
"TRAIN_DYNAMICS_EVERY_REAL_SAMPLE_COUNT": 10,
"START_TRAIN_ALGO_AFTER_SAMPLE_COUNT": 1,
"START_TRAIN_DYNAMICS_AFTER_SAMPLE_COUNT": 1,
"START_TEST_ALGO_AFTER_SAMPLE_COUNT": 1,
"START_TEST_DYNAMICS_AFTER_SAMPLE_COUNT": 1,
"WARM_UP_DYNAMICS_SAMPLES": 1
},
func_dict={
'train_algo': {
'func': agent.train,
'args': list(),
'kwargs': dict(state='state_agent_training')
},
'train_algo_from_synthesized_data': {
'func': agent.train,
'args': list(),
'kwargs': dict(state='state_agent_training')
},
'train_dynamics': {
'func': agent.train,
'args': list(),
'kwargs': dict(state='state_dynamics_training')
},
'test_algo': {
'func': agent.test,
'args': list(),
'kwargs': dict(sample_count=10)
},
'test_dynamics': {
'func': agent.algo.test_dynamics,
'args': list(),
'kwargs': dict(sample_count=10, env=env)
},
'sample_from_real_env': {
'func':
agent.sample,
'args':
list(),
'kwargs':
dict(sample_count=10,
env=agent.env,
in_which_status='TRAIN',
store_flag=True)
},
'sample_from_dynamics_env': {
'func':
agent.sample,
'args':
list(),
'kwargs':
dict(sample_count=10,
env=agent.algo.dynamics_env,
in_which_status='TRAIN',
store_flag=True)
}
})
experiment = Experiment(tuner=None,
env=env,
agent=agent,
flow=flow,
name=name + '_exp')
experiment.run()
def pendulum_task_fn():
exp_config = PENDULUM_BENCHMARK_CONFIG_DICT
GlobalConfig().set('DEFAULT_EXPERIMENT_END_POINT',
exp_config['DEFAULT_EXPERIMENT_END_POINT'])
env = make('Pendulum-v0')
name = 'benchmark'
env_spec = EnvSpec(obs_space=env.observation_space,
action_space=env.action_space)
mlp_dyna = ContinuousMLPGlobalDynamicsModel(
env_spec=env_spec,
name_scope=name + '_mlp_dyna',
name=name + '_mlp_dyna',
**exp_config['DynamicsModel']
)
algo = ModelPredictiveControl(
dynamics_model=mlp_dyna,
env_spec=env_spec,
config_or_config_dict=exp_config['MPC'],
name=name + '_mpc',
policy=UniformRandomPolicy(env_spec=env_spec, name='uni_policy')
)
algo.set_terminal_reward_function_for_dynamics_env(reward_func=REWARD_FUNC_DICT['Pendulum-v0'](),
terminal_func=FixedEpisodeLengthTerminalFunc(
max_step_length=env.unwrapped._max_episode_steps,
step_count_fn=algo.dynamics_env.total_step_count_fn), )
agent = Agent(env=env, env_spec=env_spec,
algo=algo,
exploration_strategy=None,
noise_adder=None,
name=name + '_agent')
flow = DynaFlow(
train_sample_count_func=lambda: get_global_status_collect()('TOTAL_AGENT_TRAIN_SAMPLE_COUNT'),
config_or_config_dict=exp_config['DynaFlow'],
func_dict={
'train_dynamics': {'func': agent.train,
'args': list(),
'kwargs': dict()},
'train_algo': None,
'test_algo': {'func': agent.test,
'args': list(),
'kwargs': dict(sample_count=1, sample_trajectory_flag=True)},
'test_dynamics': {'func': agent.algo.test_dynamics,
'args': list(),
'kwargs': dict(sample_count=100, env=env)},
'sample_from_real_env': {'func': agent.sample,
'args': list(),
'kwargs': dict(sample_count=10,
env=agent.env,
in_which_status='TRAIN',
store_flag=True)},
'sample_from_dynamics_env': None,
'train_algo_from_synthesized_data': None
}
)
experiment = Experiment(
tuner=None,
env=env,
agent=agent,
flow=flow,
name=name
)
experiment.run()
def pendulum_task_fn():
exp_config = PENDULUM_BENCHMARK_CONFIG_DICT
GlobalConfig().set('DEFAULT_EXPERIMENT_END_POINT',
exp_config['DEFAULT_EXPERIMENT_END_POINT'])
env = make('Pendulum-v0')
name = 'benchmark'
env_spec = EnvSpec(obs_space=env.observation_space,
action_space=env.action_space)
mlp_dyna = ContinuousMLPGlobalDynamicsModel(
env_spec=env_spec,
name_scope=name + '_mlp_dyna',
name=name + '_mlp_dyna',
output_low=env_spec.obs_space.low,
output_high=env_spec.obs_space.high,
**exp_config['DynamicsModel'])
dyna_env = DynamicsEnvWrapper(mlp_dyna)
dyna_env.set_terminal_reward_func(
terminal_func=FixedEpisodeLengthTerminalFunc(
max_step_length=env.unwrapped._max_episode_steps,
step_count_fn=dyna_env.total_step_count_fn),
reward_func=REWARD_FUNC_DICT['Pendulum-v0']())
policy = iLQRPolicy(env_spec=env_spec,
**exp_config['ILQR'],
dynamics=dyna_env,
cost_fn=RewardFuncCostWrapper(
reward_func=REWARD_FUNC_DICT['Pendulum-v0']()))
algo = iLQRAlogWrapper(policy=policy,
env_spec=env_spec,
dynamics_env=dyna_env)
agent = Agent(env=env,
env_spec=env_spec,
algo=algo,
exploration_strategy=None,
noise_adder=None,
name=name + '_agent')
flow = DynaFlow(
train_sample_count_func=lambda: get_global_status_collect()
('TOTAL_AGENT_TRAIN_SAMPLE_COUNT'),
config_or_config_dict=exp_config['DynaFlow'],
func_dict={
'train_dynamics': {
'func': agent.train,
'args': list(),
'kwargs': dict(state='state_dynamics_training')
},
'train_algo': None,
'test_algo': {
'func': agent.test,
'args': list(),
'kwargs': dict(sample_count=1, sample_trajectory_flag=True)
},
'test_dynamics': {
'func': agent.algo.test_dynamics,
'args': list(),
'kwargs': dict(sample_count=100, env=env)
},
'sample_from_real_env': {
'func':
agent.sample,
'args':
list(),
'kwargs':
dict(sample_count=10,
env=agent.env,
in_which_status='TRAIN',
store_flag=True)
},
'sample_from_dynamics_env': None,
'train_algo_from_synthesized_data': None
})
experiment = Experiment(tuner=None,
env=env,
agent=agent,
flow=flow,
name=name)
experiment.run()