def generate(parameter_distribution,
num_episodes,
env_update_fn,
filepath=None,
n_cpu=6):
env_name = 'CartPole-v1'
model_dir = os.path.join(os.getcwd(), 'models')
model_path = os.path.join(model_dir, 'ppo2_' + env_name + '.pkl')
os.makedirs(model_dir, exist_ok=True)
os.makedirs(os.path.dirname(filepath), exist_ok=True)
def make_env(env_name):
env = gym.make(env_name)
return env
env = SubprocVecEnv([lambda: make_env(env_name) for i in range(n_cpu)])
try:
model = PPO2.load(model_path)
except Exception as e:
trainer = CartPoleTrainer(env)
model = trainer.train(model_path)
obs = env.reset()
env = make_env(env_name)
states, actions, next_states, parameters, steps = [], [], [], [], []
for ep in range(num_episodes):
obs = env.reset()
params = parameter_distribution()
env_update_fn(env.unwrapped, params)
done = False
step = 0
while not done:
action, _states = model.predict(obs)
states.append(obs)
actions.append([action])
obs, reward, done, info = env.step(action)
next_states.append(obs)
parameters.append(params)
steps.append(step)
step += 1
data = {
'states': np.array(states),
'actions': np.array(actions),
'next_states': np.array(next_states),
'parameters': np.array(parameters),
'steps': np.array(steps)
}
if filepath:
print('filepath: ', filepath)
with open(filepath, 'wb') as f:
np.save(filepath, data)
return data
def play(env_name, seed, load_file, total_timesteps, n_cpu):
np.set_printoptions(precision=5)
def padding_obss(obss, dummy_obss):
dummy_obss[ 0, :, :, : ] = obss
return dummy_obss
# trained LSTM model cannot change number of env.
# so it needs to reshape observation by padding dummy data.
dummy_obss = np.zeros((n_cpu, 64, 64, 4))
env = SubprocVecEnv([make_env(env_name, 0, seed)])
model = PPO2.load(load_file, verbose=1)
obss = env.reset()
obss = padding_obss(obss, dummy_obss)
rewards_buf = []
steps_buf = []
# TODO: single
for i in range(total_timesteps):
actions, _states = model.predict(obss)
actions = actions[0:1]
obss, rewards, dones, infos = env.step(actions)
obss = padding_obss(obss, dummy_obss)
# env.render() # dummy
if dones[0]:
rewards_buf.append(infos[0]['episode']['r'])
steps_buf.append(infos[0]['episode']['l'])
line = np.array([np.mean(rewards_buf), np.std(rewards_buf), np.mean(steps_buf), np.std(steps_buf)])
print(len(rewards_buf), line)
obss = env.reset()
obss = padding_obss(obss, dummy_obss)
env.close()
def optimize_params(self,
trial,
n_prune_evals_per_trial: int = 2,
n_tests_per_eval: int = 1):
train_provider, test_provider = self.data_provider.split_data_train_test(
self.train_split_percentage)
train_provider, validation_provider = train_provider.split_data_train_test(
self.train_split_percentage)
del test_provider
train_env = SubprocVecEnv(
[make_env(train_provider, i) for i in range(1)])
validation_env = SubprocVecEnv(
[make_env(validation_provider, i) for i in range(1)])
model_params = self.optimize_agent_params(trial)
model = self.Model(self.Policy,
train_env,
verbose=self.model_verbose,
nminibatches=1,
tensorboard_log=self.tensorboard_path,
**model_params)
last_reward = -np.finfo(np.float16).max
n_steps_per_eval = int(
len(train_provider.data_frame) / n_prune_evals_per_trial)
for eval_idx in range(n_prune_evals_per_trial):
try:
model.learn(n_steps_per_eval)
except AssertionError:
raise
rewards = []
n_episodes, reward_sum = 0, 0.0
state = None
obs = validation_env.reset()
while n_episodes < n_tests_per_eval:
action, state = model.predict(obs, state=state)
obs, reward, done, _ = validation_env.step(action)
reward_sum += reward
if all(done):
rewards.append(reward_sum)
reward_sum = 0.0
n_episodes += 1
obs = validation_env.reset()
last_reward = np.mean(rewards)
trial.report(-1 * last_reward, eval_idx)
if trial.should_prune(eval_idx):
raise optuna.structs.TrialPruned()
return -1 * last_reward
class SimulatorModel(object):
def __init__(self, _make_env_func, parallel_agents):
"""
This class instantiates a dynamics model based on the pybullet simulator
(i.e: simulates exactly the result of the actions), it can be used
for reward tuning and verifying tasks..etc
:param _make_env_func: (func) a function if called it will return a gym
environment.
:param parallel_agents: (int) number of parallel agents to siumulate
to evaluate the actions.
"""
self.parallel_agents = parallel_agents
self.envs = SubprocVecEnv(
[_make_env_func() for i in range(self.parallel_agents)])
return
def evaluate_trajectories(self, action_sequences):
"""
A function to be called to evaluate the action sequences and return
the corresponding reward for each sequence.
:param action_sequences: (nd.array) actions to be evaluated
(number of sequences, horizon length)
:return: (nd.array) sum of rewards for each action sequence.
"""
horizon_length = action_sequences.shape[1]
num_of_particles = action_sequences.shape[0]
rewards = np.zeros([num_of_particles])
assert ((float(num_of_particles) / self.parallel_agents).is_integer())
for j in range(0, num_of_particles, self.parallel_agents):
self.envs.reset()
total_reward = np.zeros([self.parallel_agents])
for k in range(horizon_length):
actions = action_sequences[j:j + self.parallel_agents, k]
task_observations, current_reward, done, info = \
self.envs.step(actions)
total_reward += current_reward
rewards[j:j + self.parallel_agents] = total_reward
return rewards
def end_sim(self):
"""
Closes the environments that were used for simulation.
:return:
"""
self.envs.close()
return
def test(self, model_epoch: int = 0, should_render: bool = True):
train_provider, test_provider = self.data_provider.split_data_train_test(self.train_split_percentage)
del train_provider
test_env = SubprocVecEnv([make_env(test_provider, i) for i in range(self.n_envs)])
model_path = path.join('data', 'agents', f'{self.study_name}__{model_epoch}.pkl')
model = self.Model.load(model_path, env=test_env)
self.logger.info(f'Testing model ({self.study_name}__{model_epoch})')
state = None
obs, done, rewards = test_env.reset(), [False], []
while not all(done):
action, state = model.predict(obs, state=state)
obs, reward, done, _ = test_env.step(action)
rewards.append(reward)
if should_render and self.n_envs == 1:
test_env.render(mode='human')
self.logger.info(
f'Finished testing model ({self.study_name}__{model_epoch}): ${"{:.2f}".format(np.sum(rewards))}')
def main():
#env = SubprocVecEnv([(lambda i=i: SwocGym(i+1, GameServicePath, i, fieldWidth=10, fieldHeight=10, saveEpisode=True)) for i in range(1)])
env = SubprocVecEnv([
(lambda i=i: MazeGym(mazeWidth=10, mazeHeight=10, nrWallsToRemove=10))
for i in range(1)
])
try:
model = PPO2("MlpPolicy",
env,
verbose=1,
tensorboard_log='/home/ralph/swoc2019/log')
if SaveFile.exists():
print('loading...')
model.load_parameters(SaveFile)
else:
print('Warning: No save file loaded')
print('evaluating...', end='')
obs = env.reset()
totalRewards = None
for i in range(100):
action, _states = model.predict(obs)
obs, rewards, dones, info = env.step(action)
totalRewards = totalRewards + rewards if totalRewards is not None else rewards
env.render()
sleep(0.2)
print(f'mean reward: {np.mean(totalRewards)}')
except KeyboardInterrupt:
print('closing...')
finally:
env.close()
print('closed')
def test(model_name, env_name, num_cpu, log_dir):
env = SubprocVecEnv([
make_football_env(env_name, i, log_dir, useMonitor=False)
for i in range(num_cpu)
])
# env = Monitor(env, log_dir, allow_early_resets=True)
model = get_model(model_name, env, log_dir)
model = model.load(log_dir + model_name + '_' + env_name, env=env)
obs = env.reset()
from matplotlib import pyplot as plt
show_num = 1
while True:
action, _states = model.predict(obs)
# obs, rewards, done, info = env.step([int(input('action:'))]*num_cpu)
obs, rewards, done, info = env.step(action)
img = obs[show_num, :, :, :]
fig = plt.figure(0)
plt.clf()
plt.imshow(img / 255)
fig.canvas.draw()
# env.render()
plt.pause(0.000001)
def play(env_name, seed, load_file, total_timesteps, n_cpu):
np.set_printoptions(precision=5)
def padding_obss(obss, dummy_obss):
dummy_obss[ 0, :, :, : ] = obss
return dummy_obss
# if it's GUI mode, number of env is changed to 1 to reduce GUI windows.
# but trained LSTM model cannot change number of env.
# so it needs to reshape observation by padding dummy data.
isGUI = env_name.find('GUI') != -1
dummy_obss = np.zeros((n_cpu, 64, 64, 4)) if isGUI else None
env = SubprocVecEnv([make_env(env_name, i, seed) for i in range(1 if isGUI else n_cpu)])
model = PPO2.load(load_file, verbose=1)
obss = env.reset()
obss = padding_obss(obss, dummy_obss) if isGUI else obss
rewards_buf = []
steps_buf = []
# TODO: single
for i in range(total_timesteps):
actions, _states = model.predict(obss)
actions = actions[0:1] if isGUI else actions
obss, rewards, dones, infos = env.step(actions)
obss = padding_obss(obss, dummy_obss) if isGUI else obss
# env.render() # dummy
if dones.any():
rewards_buf.extend([ info['episode']['r'] for info in infos if 'episode' in info ])
steps_buf.extend([ info['episode']['l'] for info in infos if 'episode' in info ])
line = np.array([np.mean(rewards_buf), np.std(rewards_buf), np.mean(steps_buf), np.std(steps_buf)])
print(len(rewards_buf), line)
env.close()
def test(test_data, model_location):
# Using a different environment to test the model
env_test = SubprocVecEnv(
[lambda: ExchangeEnv.ExchangeEnv(test_data, 10000, 0)])
model = PPO2.load(model_location)
obs = env_test.reset()
done = False
price_history = []
portfolio_value = []
while not done:
action, _states = model.predict(obs)
obs, rewards, done, _ = env_test.step(action)
# Appending the current time steps highest bid
price_history.append(obs[0][0][0])
# Appending current portfolio value
portfolio_value.append(rewards[0])
with open("price_history.txt", "w") as f:
writer = csv.writer(f)
writer.writerow(price_history)
with open("portfolio_value.txt", "w") as f:
writer = csv.writer(f)
writer.writerow(portfolio_value)
def main(mode="train"):
n_cpu = 2
env = SubprocVecEnv(
[lambda: gym.make('balancebot-continuum-v0') for i in range(n_cpu)])
if mode == "train":
model = ppo2(policy=MlpPolicy,
env=env,
learning_rate=1e-3,
verbose=0,
full_tensorboard_log=False,
tensorboard_log="./ppo2_balancebot_tensorboard")
model.learn(total_timesteps=100000, callback=callback)
print("Saving model to ppo2_balance_continuum.pkl")
model.save("ppo2_balance_continuum.pkl")
del model # remove to demonstrate saving and loading
if mode == "test":
model = ppo2.load("ppo2_balance_continuum.pkl")
obs = env.reset()
done = [False, False]
# env.set_done(5000)
while not all(done):
action, _states = model.predict(obs)
obs, rewards, done, info = env.step(action)
# env.render()
print(obs)
def main():
num_envs = 20
num_players_per_env = 2
envs = [makeEnv for i in range(num_envs)]
actionSpace = makeEnv().action_space
env = SubprocVecEnv(envs)
env.reset()
gameFinished = [False] * num_envs
while not all(gameFinished):
inputs = []
for game in range(num_envs):
if not gameFinished[game]: inputs.append([actionSpace.sample() for player in range(num_players_per_env)])
else: inputs.append([0] * num_players_per_env)
_, _, done, info = env.step(inputs)
gameFinished = [gameElem or doneElem for gameElem, doneElem in list(zip(gameFinished, done))]
time.sleep(.0001)
def attention_render(model_name, env_name, num_cpu, log_dir):
if not os.path.exists(log_dir):
raise ('log_dir not Exists')
env_id = env_name + 'NoFrameskip-v4'
env = SubprocVecEnv([make_env(env_id, i, log_dir) for i in range(num_cpu)])
# env = Monitor(env, log_dir, allow_early_resets=True)
if model_name == 'A2C_Attention':
model = A2C(AttentionPolicy,
env,
verbose=1,
tensorboard_log=log_dir + 'tensorboard/')
elif model_name == 'A2C_Attention2':
model = A2C(Attention2Policy,
env,
verbose=1,
tensorboard_log=log_dir + 'tensorboard/')
elif model_name == 'A2C':
model = A2C(LstmPolicy,
env,
verbose=1,
tensorboard_log=log_dir + 'tensorboard/')
else:
model = None
model = model.load(log_dir + model_name + '_' + env_name, env=env)
obs = env.reset()
# print(env.observation_space)
# cv2.imshow('test', RGB2BGR(obs[0]))
# cv2.waitKey(0)
while True:
action, _states = model.predict(obs)
obs, rewards, done, info = env.step(action)
attentions = model.get_attention(obs, _states, done)[0]
attentions_img = []
# print('attention', np.array(attention).shape)
for i, attention in enumerate(attentions):
attention = np.array(attention)
attention = np.reshape(attention, [
env.observation_space.shape[0] // 10,
env.observation_space.shape[1] // 10, 1
])
attention = np.repeat(attention, [10] * attention.shape[0], axis=0)
attention = np.repeat(attention, [10] * attention.shape[1], axis=1)
attention = attention * 255
attentions_img.append(attention)
# print(np.sum(attention))
attentions = tile_images(attentions_img)
cv2.imshow('attention', attentions)
cv2.waitKey(1)
# break
env.render()
return model
def optimize_agent(trial):
""" Train the model and optimise
Optuna maximises the negative log likelihood, so we
need to negate the reward here
"""
model_params = optimize_TD3(trial)
env = SubprocVecEnv([
lambda: NormalizeActionWrapper(LearningRocket(visualize=False))
for i in range(n_cpu)
])
n_actions = env.action_space.shape[-1]
action_noise = NormalActionNoise(mean=np.zeros(n_actions),
sigma=0.1 * np.ones(n_actions))
model = TD3(MlpPolicy,
env,
action_noise=action_noise,
policy_kwargs=dict(layers=[400, 300]))
model.learn(50000)
rewards = []
n_episodes, reward_sum = 0, 0.0
obs = env.reset()
step = 0
while n_episodes < 4:
step += 1
action, _ = model.predict(obs)
obs, reward, done, _ = env.step(action)
reward_sum += reward
if done:
rewards.append(reward_sum)
reward_sum = 0.0
n_episodes += 1
obs = env.reset()
last_reward = np.mean(rewards)
trial.report(-1 * last_reward, step)
return -1 * last_reward
def run():
torch.multiprocessing.freeze_support()
env_id = "CartPole-v1"
num_cpu = 4 # Number of processes to use
# Create the vectorized environment
env = SubprocVecEnv([make_env(env_id, i) for i in range(num_cpu)])
model = ACKTR(MlpPolicy, env, verbose=1)
model.learn(total_timesteps=25000)
obs = env.reset()
for _ in range(1000):
action, _states = model.predict(obs)
obs, rewards, dones, info = env.step(action)
def _eval_model(model, env_id, ob_shape, num_eps, plot=False):
test_env = SubprocVecEnv([make_env(env_id)])
sharpe_ratios = []
for episode in range(num_eps):
# Padding zeros to the test env to match the shape of the training env.
zero_completed_obs = np.zeros((NUM_CPU,) + ob_shape)
zero_completed_obs[0, :] = test_env.reset()
state = None
for _ in range(L):
action, state = model.predict(zero_completed_obs, state=state, deterministic=True)
zero_completed_obs[0, :], reward, done, _ = test_env.env_method('step', action[0], indices=0)[0]
sharpe_ratios.append(test_env.env_method('get_sharpe_ratio', indices=0)[0])
if plot: test_env.env_method('render', indices=0)
test_env.close()
# Return the average sharpe ratio
return sum(sharpe_ratios) / len(sharpe_ratios)
def test():
# Parallel environments
n_cpu = 4
env = SubprocVecEnv([lambda: RSEnv() for i in range(n_cpu)])
model = A2C(MlpPolicy, env, verbose=1)
model.learn(total_timesteps=600000, log_interval=10)
model.save("sba2c")
env = TestRSEnv()
obs = env.reset()
done = False
while not done:
action, _ = model.predict(obs)
obs, rewards, done, info = env.step(action)
env.render()
env.close()
def objective(trial):
# Hyper-parameters to adjust
policy = trial.suggest_categorical('policy', ['MlpPolicy', 'MlpLnPolicy', 'MlpLstmPolicy', 'MlpLnLstmPolicy'])
gamma = trial.suggest_uniform('gamma', 0.10, 1.0)
ent_coef = trial.suggest_uniform('ent_coef', 0.01, 0.10)
learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-2)
vf_coef = trial.suggest_uniform('vf_coef', 0.10, 1.0)
lam = trial.suggest_uniform('lam', 0.01, 0.95)
if policy == 'MlpPolicy':
policy = MlpPolicy
elif policy == 'MlpLnPolicy':
policy = MlpLnPolicy
elif policy == 'MlpLstmPolicy':
policy = MlpLstmPolicy
elif policy == 'MlpLnLstmPolicy':
policy = MlpLnLstmPolicy
# Train with those hyper-parameters
n_cpu = 4
env = SubprocVecEnv([lambda: gimbal(5, 500) for i in range(n_cpu)])
model = PPO2(policy=policy, env=env, gamma=gamma, n_steps=100, ent_coef=ent_coef, learning_rate=learning_rate,
vf_coef=vf_coef, max_grad_norm=0.5, lam=lam, nminibatches=4, noptepochs=4, cliprange=0.2,
verbose=0, tensorboard_log=None, _init_setup_model=True, policy_kwargs=None, full_tensorboard_log=False)
model.learn(total_timesteps=250000, callback=None, seed=None, log_interval=1, tb_log_name='PPO2', reset_num_timesteps=True)
# Calculate worth
env = gimbal(5, 500)
MAX_episodes = 25
reward_avg = 0
for episodes in range(MAX_episodes):
obs = env.reset()
r = 0
while True:
action, _states = model.predict(obs)
obs, rewards, dones, info = env.step(action)
r += rewards
#env.render()
if dones:
reward_avg += r
break
return - (reward_avg / MAX_episodes)
def main():
agent_data = pd.read_csv('../output_EURUSD_M1_/agentData.csv')
agent_data = agent_data.drop(agent_data.columns[0], axis=1)
agent_data = agent_data.astype('float32')
env = SubprocVecEnv([lambda: ForexTradingEnv(agent_data)] * 10, )
#env = DummyVecEnv([lambda: ForexTradingEnv(agent_data)], )
# model = DQN(CustomDQNPolicy, env, gamma=0.95, verbose=1, tensorboard_log = "./tensorboard", entcoeff=0.005, adam_epsilon = 1e-6)
import tensorflow as tf
from TenorboardCallbacks import TensorboardCallback
checkpoint_callback = CheckpointCallback(save_freq=1000000,
save_path='./models/',
name_prefix='ppo2')
for curr in [1]:
model = PPO2(PPO2Policy_Basic,
env,
verbose=1,
tensorboard_log="./tensorboard",
vf_coef=1e-7,
ent_coef=1e-4,
n_steps=512,
gamma=0.99)
#model = PPO2.load("5_days_model/ppo2_999000000_steps.zip", policy=PPO2Policy_Basic, env = env,verbose=1, tensorboard_log = "./tensorboard")
model.learn(total_timesteps=10000000000,
log_interval=10000000,
callback=CallbackList(
[TensorboardCallback(env), checkpoint_callback]))
model.save(model_fileName)
obs = env.reset()
for i in range(2000000):
action, _states = model.predict(obs)
obs, rewards, done, info = env.step(action)
if i % 1 == 0:
env.render()
if done:
break
class Agent:
def __init__(self):
self.env = None
self.model = None
def create_env(self, game, envs, render=False, sleep=0.):
env = gym.make(game)
# env = FrameStack(env, 4)
env = CustomGym(env, render=render, sleep=sleep)
self.env = SubprocVecEnv([lambda: env for i in range(envs)])
def create_model(self):
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config):
self.model = PPO2(policy=MlpPolicy,
env=self.env,
n_steps=8192,
nminibatches=8,
lam=0.95,
gamma=0.99,
noptepochs=4,
ent_coef=0.001,
learning_rate=lambda _: 2e-5,
cliprange=lambda _: 0.2,
verbose=1,
tensorboard_log="gym_logs")
def train(self, timesteps, loops, name="agent"):
for i in range(loops):
self.model.learn(timesteps)
self.model.save(name + str(i))
def evaluate(self, timesteps, agent_name):
self.model = PPO2.load(agent_name)
obs = self.env.reset()
for i in range(timesteps):
action, _states = self.model.predict(obs)
obs, rewards, dones, info = self.env.step(action)
def run_baseline_ppo2(env_name, n_cpu=4, train=True):
from stable_baselines.common.policies import MlpPolicy
from stable_baselines.common.vec_env import SubprocVecEnv
from stable_baselines import PPO2
if train:
# multiprocess environment
env = SubprocVecEnv([lambda: gym.make(env_name) for i in range(n_cpu)])
model = PPO2(MlpPolicy, env, verbose=1)
model.learn(total_timesteps=100000)
model.save("checkpoints/ppo2_" + env_name)
else:
from stable_baselines.common.vec_env import DummyVecEnv
env = DummyVecEnv([lambda: gym.make(env_name)])
model = PPO2.load("checkpoints/ppo2_" + env_name)
obs = env.reset()
while True:
action, _states = model.predict(obs)
obs, rewards, dones, info = env.step(action)
env.render()
print("state: ", obs, " reward: ", rewards, " done: ", dones, "info: ", info)
class BitmexTradingStrategySBL(TradingStrategy):
"""A trading strategy capable of self tuning, training, and evaluating with stable-baselines.
Arguments:
environments: An instance of a trading environments for the agent to trade within.
model: The RL model to create the agent with.
Defaults to DQN.
policy: The RL policy to train the agent's model with.
Defaults to 'MlpPolicy'.
model_kwargs: Any additional keyword arguments to adjust the model.
kwargs: Optional keyword arguments to adjust the strategy.
"""
def __init__(self,
environment: BitmexEnvironment,
model: BaseRLModel = DQN,
policy: Union[str, BasePolicy] = 'MlpPolicy',
model_kwargs: any = {},
policy_kwargs: any = {},
n_env: int = 1,
**kwargs):
self._model = model
self._model_kwargs = model_kwargs
self._policy_kwargs = policy_kwargs
self._n_env = n_env
self.environment = environment
self._agent = self._model(policy,
self._environment,
**self._model_kwargs,
policy_kwargs=self._policy_kwargs)
@property
def environment(self) -> 'BitmexEnvironment':
"""A `BitmexEnvironment` instance for the agent to trade within."""
return self._environment
@environment.setter
def environment(self, environment: 'BitmexEnvironment'):
envs = [lambda: environment for _ in range(self._n_env)]
if self._n_env == 1:
self._environment = DummyVecEnv(envs)
else:
self._environment = SubprocVecEnv(envs)
def restore_agent(self, path: str, custom_objects: any = {}):
"""Deserialize the strategy's learning agent from a file.
Arguments:
path: The `str` path of the file the agent specification is stored in.
"""
self._custom_objects = custom_objects
self._agent = self._model.load(path,
env=self._environment,
custom_objects=self._custom_objects,
kwargs=self._model_kwargs)
def save_agent(self, path: str):
"""Serialize the learning agent to a file for restoring later.
Arguments:
path: The `str` path of the file to store the agent specification in.
"""
self._agent.save(path)
def tune(self,
steps: int = None,
episodes: int = None,
callback: Callable[[pd.DataFrame], bool] = None) -> pd.DataFrame:
raise NotImplementedError
def _train_callback(self, _locals, _globals):
# performance = self._environment.performance
#
# if self._episode_callback and self._environment.done():
# self._episode_callback(performance)
return True
def train(
self,
steps: int = None,
episodes: int = None,
render_mode: str = None,
episode_callback: Callable[[pd.DataFrame],
bool] = None) -> pd.DataFrame:
if steps is None:
raise ValueError(
'You must set the number of `steps` to train the strategy.')
self._agent.learn(steps, callback=self._train_callback)
return True
def test(
self,
steps: int = None,
episodes=None,
render_mode: str = None,
episode_callback: Callable[[pd.DataFrame],
bool] = None) -> pd.DataFrame:
if steps is None and episodes is None:
raise ValueError(
'You must set the number of `steps` or `episodes` to test the strategy.'
)
steps_completed, episodes_completed, average_reward = 0, 0, 0
obs, state, dones = self._environment.reset(), None, [False]
performance = {}
while (steps is not None and
(steps == 0 or steps_completed < steps)) or (
episodes is not None and episodes_completed < episodes):
actions, state = self._agent.predict(obs, state=state, mask=dones)
# actions, state = self._agent.predict(obs)
obs, rewards, dones, info = self._environment.step(actions)
steps_completed += 1
average_reward -= average_reward / steps_completed
average_reward += rewards[0] / (steps_completed + 1)
exchange_performance = info[0].get('exchange').performance
performance = exchange_performance if len(
exchange_performance) > 0 else performance
if render_mode is not None:
self._environment.render(mode=render_mode)
if dones[0]:
if episode_callback is not None and not episode_callback(
performance):
break
episodes_completed += 1
obs = self._environment.reset()
print("Finished running strategy.")
print("Total episodes: {} ({} timesteps).".format(
episodes_completed, steps_completed))
print("Average reward: {}.".format(average_reward))
return performance
env = SubprocVecEnv([make_env(x) for x in range(num_envs)],
start_method='forkserver')
# env.get_valid_actions = lambda: np.array([e.get_valid_actions() for e in env.envs])
env.get_valid_actions = lambda: np.array(
env.env_method('get_valid_actions'))
model = algo.MaskedPPO(CustomLSTMPolicy,
env,
verbose=1,
n_steps=20,
nminibatches=batch_size,
tensorboard_log="../out/meta_opt/")
model.learn(total_timesteps=100000, log_interval=10)
model.save('meta_optimizer')
obs = env.reset()
state = None
total_rewards = 0
done = [False for _ in range(env.num_envs)]
for i in range(1000):
action, _states = model.predict(obs, state=state, mask=done)
obs, rewards, done, info = env.step(action)
total_rewards += rewards
# if done:
# break
print(total_rewards)
def run(alg,
alg_kwargs,
task,
task_kwargs,
wrappers_kwargs,
expl_params,
rollout,
num_trials,
folder,
n_thrds,
n_lstm,
rerun=False,
test_kwargs={},
num_retrains=10,
seed=0,
train_mode=None,
sl_kwargs=None):
train_mode = train_mode or 'RL'
env = test_env(task, kwargs=task_kwargs, num_steps=1000)
num_timesteps = int(1000 * num_trials / (env.num_tr))
files = glob.glob(folder + '/*model*')
vars_ = {
'alg': alg,
'alg_kwargs': alg_kwargs,
'task': task,
'task_kwargs': task_kwargs,
'wrappers_kwargs': wrappers_kwargs,
'expl_params': expl_params,
'rollout': rollout,
'folder': folder,
'num_trials': num_trials,
'n_thrds': n_thrds,
'n_lstm': n_lstm
}
np.savez(folder + '/params.npz', **vars_)
if len(files) == 0 or rerun:
if train_mode == 'RL':
if alg == "A2C":
from stable_baselines import A2C as algo
elif alg == "ACER":
from stable_baselines import ACER as algo
elif alg == "ACKTR":
from stable_baselines import ACKTR as algo
elif alg == "PPO2":
from stable_baselines import PPO2 as algo
env = SubprocVecEnv([
make_env(env_id=task,
rank=i,
seed=seed,
wrapps=wrappers_kwargs,
**task_kwargs) for i in range(n_thrds)
])
model = algo(LstmPolicy,
env,
verbose=0,
n_steps=rollout,
n_cpu_tf_sess=n_thrds,
tensorboard_log=None,
policy_kwargs={
"feature_extraction": "mlp",
"n_lstm": n_lstm
},
**alg_kwargs)
# this assumes 1 trial ~ 10 steps
sv_freq = 5 * wrappers_kwargs['MonitorExtended-v0']['sv_per']
chckpnt_cllbck = CheckpointCallback(save_freq=sv_freq,
save_path=folder,
name_prefix='model')
model.learn(total_timesteps=num_timesteps, callback=chckpnt_cllbck)
model.save(f"{folder}/model_{num_timesteps}_steps.zip")
plotting.plot_rew_across_training(folder=folder)
elif train_mode == 'SL':
stps_ep = sl_kwargs['steps_per_epoch']
wraps_sl = deepc(wrappers_kwargs)
del wraps_sl['PassAction-v0']
del wraps_sl['PassReward-v0']
del wraps_sl['MonitorExtended-v0']
env = make_env(env_id=task,
rank=0,
seed=seed,
wrapps=wraps_sl,
**task_kwargs)()
dataset = ngym.Dataset(env,
batch_size=sl_kwargs['btch_s'],
seq_len=rollout,
batch_first=True)
obs_size = env.observation_space.shape[0]
act_size = env.action_space.n
model = define_model(seq_len=rollout,
num_h=n_lstm,
obs_size=obs_size,
act_size=act_size,
batch_size=sl_kwargs['btch_s'],
stateful=sl_kwargs['stateful'],
loss=sl_kwargs['loss'])
# Train network
data_generator = (dataset() for i in range(stps_ep))
model.fit(data_generator, verbose=1, steps_per_epoch=stps_ep)
model.save(f"{folder}/model_{stps_ep}_steps")
if len(test_kwargs) != 0:
for key in test_kwargs.keys():
sv_folder = folder + key
test_kwargs[key]['seed'] = seed
if train_mode == 'RL':
if '_all' not in key:
ga.get_activity(folder, alg, sv_folder, **test_kwargs[key])
else:
files = glob.glob(folder + '/model_*_steps.zip')
for f in files:
model_name = os.path.basename(f)
sv_f = folder + key + '_' + model_name[:-4]
ga.get_activity(folder,
alg,
sv_folder=sv_f,
model_name=model_name,
**test_kwargs[key])
elif train_mode == 'SL':
stps_ep = sl_kwargs['steps_per_epoch']
wraps_sl = deepc(wrappers_kwargs)
wraps_sl.update(test_kwargs[key]['wrappers'])
del wraps_sl['PassAction-v0']
del wraps_sl['PassReward-v0']
env = make_env(env_id=task,
rank=0,
seed=seed,
wrapps=wraps_sl,
**task_kwargs)()
obs_size = env.observation_space.shape[0]
act_size = env.action_space.n
model_test = define_model(seq_len=1,
batch_size=1,
obs_size=obs_size,
act_size=act_size,
stateful=sl_kwargs['stateful'],
num_h=n_lstm,
loss=sl_kwargs['loss'])
ld_f = folder + 'model_' + str(stps_ep) + '_steps'.replace(
'//', '/')
model_test.load_weights(ld_f)
env.reset()
for ind_stp in range(sl_kwargs['test_steps']):
obs = env.ob_now
obs = obs[np.newaxis]
obs = obs[np.newaxis]
action = model_test.predict(obs)
action = np.argmax(action, axis=-1)[0]
_, _, _, _ = env.step(action)
import retro
from stable_baselines.common.vec_env import SubprocVecEnv, DummyVecEnv, VecFrameStack, VecNormalize
from stable_baselines import PPO2, A2C
import numpy as np
import gym
from stable_baselines.common.callbacks import CheckpointCallback
from utils import *
if __name__ == "__main__":
num_envs = 16 # Must use the save number of envs as trained on but we create a single dummy env for testing.
envs = SubprocVecEnv([make_env] * num_envs)
envs = VecFrameStack(envs, n_stack=4)
model = PPO2.load("./subzero_model.zip")
model.set_env(envs)
obs = envs.reset()
print(obs.shape)
# Create one env for testing
env = DummyVecEnv([make_env])
env = VecFrameStack(env, n_stack=4)
obs = env.reset()
# model.predict(test_obs) would through an error
# because the number of test env is different from the number of training env
# so we need to complete the observation with zeroes
zero_completed_obs = np.zeros((num_envs, ) + envs.observation_space.shape)
zero_completed_obs[0, :] = obs
obs = zero_completed_obs
while True:
class PPO2_SB():
def __init__(self):
self.love = 'Ramona'
self.env_fns = []
self.env_names = []
self.environs = [
'SpringYardZone.Act3', 'SpringYardZone.Act2', 'GreenHillZone.Act3',
'GreenHillZone.Act1', 'StarLightZone.Act2', 'StarLightZone.Act1',
'MarbleZone.Act2', 'MarbleZone.Act1', 'MarbleZone.Act3',
'ScrapBrainZone.Act2', 'LabyrinthZone.Act2', 'LabyrinthZone.Act1',
'LabyrinthZone.Act3', 'SpringYardZone.Act1', 'GreenHillZone.Act2',
'StarLightZone.Act3', 'ScrapBrainZone.Act1'
]
self.environsv2 = ['1Player.Axel.Level1']
self.generate_expert_traj = generate_expert_traj
def create_envs(self, game_name, state_name, num_env):
for i in range(num_env):
self.env_fns.append(
partial(make_env, game=game_name, state=state_name))
self.env_names.append(game_name + '-' + state_name)
self.env = SubprocVecEnv(self.env_fns)
def train(self,
game,
state,
num_e=1,
n_timesteps=25000000,
save='default2'):
self.create_envs(game_name=game, state_name=state, num_env=num_e)
#self.model = PPO2.load("default2", SubprocVecEnv(self.env_fns), policy=CnnPolicy, tensorboard_log="./sonic/" )
#self.model = PPO2(CnnPolicy, SubprocVecEnv(self.env_fns), learning_rate=1e-5, verbose=1,tensorboard_log="./sonic/" )
self.model = PPO2(policy=CnnPolicy,
env=SubprocVecEnv(self.env_fns),
n_steps=8192,
nminibatches=8,
lam=0.95,
gamma=0.99,
noptepochs=4,
ent_coef=0.001,
learning_rate=lambda _: 2e-5,
cliprange=lambda _: 0.2,
verbose=1,
tensorboard_log="./sonic/")
self.model.learn(n_timesteps)
self.model.save(save)
self.model.learn(n_timesteps)
self.model.save(save + '2')
self.model.learn(n_timesteps)
self.model.save(save + '3')
self.model.learn(n_timesteps)
self.model.save(save + '4')
def evaluate(self, game, state, num_e=1, num_steps=14400):
"""
Evaluate a RL agent
:param model: (BaseRLModel object) the RL Agent
:param num_steps: (int) number of timesteps to evaluate it
:return: (float) Mean reward
"""
self.create_envs(game_name=game, state_name=state, num_env=num_e)
self.model = PPO2.load("default2",
SubprocVecEnv(self.env_fns),
policy=CnnPolicy,
tensorboard_log="./sonic/")
episode_rewards = [[0.0] for _ in range(self.env.num_envs)]
obs = self.env.reset()
for i in range(num_steps):
# _states are only useful when using LSTM policies
actions, _states = self.model.predict(obs)
# # here, action, rewards and dones are arrays
# # because we are using vectorized env
obs, rewards, dones, info = self.env.step(actions)
# Stats
for i in range(self.env.num_envs):
episode_rewards[i][-1] += rewards[i]
if dones[i]:
episode_rewards[i].append(0.0)
mean_rewards = [0.0 for _ in range(self.env.num_envs)]
n_episodes = 0
for i in range(self.env.num_envs):
mean_rewards[i] = np.mean(episode_rewards[i])
n_episodes += len(episode_rewards[i])
# Compute mean reward
mean_reward = np.mean(mean_rewards)
print("Mean reward:", mean_reward, "Num episodes:", n_episodes)
return mean_reward
def pre_train(self):
# Using only one expert trajectory
# you can specify `traj_limitation=-1` for using the whole dataset
dataset = ExpertDataset(expert_path='expert_cartpole.npz',
traj_limitation=1,
batch_size=128)
model = PPO2('MlpPolicy', 'CartPole-v1', verbose=1)
# Pretrain the PPO2 model
model.pretrain(dataset, n_epochs=1000)
# As an option, you can train the RL agent
# model.learn(int(1e5))
# Test the pre-trained model
env = model.get_env()
obs = env.reset()
reward_sum = 0.0
for _ in range(1000):
action, _ = model.predict(obs)
obs, reward, done, _ = env.step(action)
reward_sum += reward
env.render()
if done:
print(reward_sum)
reward_sum = 0.0
obs = env.reset()
env.close()
def gen_pre_train(self,
game,
state,
num_e=1,
save='default2',
episodes=10):
self.create_envs(game_name=game, state_name=state, num_env=num_e)
env = SubprocVecEnv(self.env_fns)
self.expert_agent = "moose"
self.generate_expert_traj(self.expert_agent,
save,
env,
n_episodes=episodes)
:param num_env: (int) the number of environments you wish to have in subprocesses
:param seed: (int) the inital seed for RNG
:param rank: (int) index of the subprocess
"""
def _init():
env = gym.make(env_id)
env.seed(seed + rank)
return env
set_global_seeds(seed)
return _init
if __name__ == '__main__':
env_id = "HumanoidPyBulletEnv-v0"
num_cpu = 1
env = SubprocVecEnv([make_env(env_id, i) for i in range(num_cpu)])
model = PPO2.load("HumanoidPyBulletEnv-v0_PPO2_2020_11_3016_29_44")
ob = env.reset()
reward = 0
while True:
action, _states = model.predict(ob)
ob, r, done, info = env.step(action)
reward += r
time.sleep(0.01)
if done:
ob = env.reset()
print('r is {}'.format(r))
print('Episode reward is {}'.format(reward))
reward = 0
class PPO2_SB():
def __init__(self):
self.love = 'Ramona'
self.env_fns = []
self.env_names = []
def make_env(self, env_id, rank, seed=0):
"""
Utility function for multiprocessed env.
:param env_id: (str) the environment ID
:param num_env: (int) the number of environment you wish to have in subprocesses
:param seed: (int) the inital seed for RNG
:param rank: (int) index of the subprocess
"""
def _init():
env = Template_Gym()
env.seed(seed + rank)
return env
set_global_seeds(seed)
return _init
def train(self, num_e=1, n_timesteps=1000000, save='saves/agent4'):
env_id = "default"
num_e = 1 # Number of processes to use
# Create the vectorized environment
#env = DummyVecEnv([lambda: env])
self.env = SubprocVecEnv(
[self.make_env(env_id, i) for i in range(num_e)])
self.env = VecNormalize(self.env, norm_obs=True, norm_reward=True)
#self.model = PPO2(policy=CnnPolicy,
#env=SubprocVecEnv(self.env_fns),
#n_steps=8192,
#nminibatches=8,
#lam=0.95,
#gamma=0.99,
#noptepochs=4,
#ent_coef=0.001,
#learning_rate=lambda _: 2e-5,
#cliprange=lambda _: 0.2,
#verbose=1,
#tensorboard_log="./breakorbust")
self.model = PPO2(CustomPolicy,
env=self.env,
verbose=0,
learning_rate=1e-5,
tensorboard_log=save)
for i in range(10):
self.model.learn(n_timesteps)
self.model.save(save)
def evaluate(self, num_env=1, num_steps=14400):
"""
Evaluate a RL agent
:param model: (BaseRLModel object) the RL Agent
:param num_steps: (int) number of timesteps to evaluate it
:return: (float) Mean reward
"""
env_id = "default"
num_e = 1
self.env = SubprocVecEnv(
[self.make_env(env_id, i) for i in range(num_e)])
self.model = PPO2.load('saves/agent.pkl',
self.env,
policy=CustomPolicy,
tensorboard_log="./ppocnn/")
episode_rewards = [[0.0] for _ in range(self.env.num_envs)]
obs = self.env.reset()
for i in range(num_steps):
# _states are only useful when using LSTM policies
actions, _states = self.model.predict(obs)
# # here, action, rewards and dones are arrays
# # because we are using vectorized env
obs, rewards, dones, info = self.env.step(actions)
# Stats
for i in range(self.env.num_envs):
episode_rewards[i][-1] += rewards[i]
if dones[i]:
episode_rewards[i].append(0.0)
mean_rewards = [0.0 for _ in range(self.env.num_envs)]
n_episodes = 0
for i in range(self.env.num_envs):
mean_rewards[i] = np.mean(episode_rewards[i])
n_episodes += len(episode_rewards[i])
# Compute mean reward
mean_reward = np.mean(mean_rewards)
print("Mean reward:", mean_reward, "Num episodes:", n_episodes)
return mean_reward
import gym
import os
from stable_baselines.common.policies import MlpPolicy
from stable_baselines.common.vec_env import SubprocVecEnv
from stable_baselines import PPO2
from env import OsmoEnv
if __name__ == '__main__':
env = SubprocVecEnv([lambda: OsmoEnv() for i in range(os.cpu_count())])
# model = PPO2(MlpPolicy, env, verbose=1, learning_rate=1e-4)
# model.learn(total_timesteps=25000)
# model.save('PPO2_baselines')
model = PPO2.load('PPO2_baselines')
model.set_env(env)
model.learning_rate = 1e-5
model.learn(total_timesteps=30000)
model.save('PP02_baselines')
env = OsmoEnv()
for i in range(10):
observation = env.reset()
done = False
while not done:
action, _ = model.predict(observation)
observation, _, done, info = env.step(action)
else:
print(info)
# 学習設定
train = False # 学習をするかどうか
validation = True # 学習結果を使って評価をするかどうか
#env_name = 'RoboschoolHumanoid-v1'
#env_name = 'RoboschoolWalker2d-v1'
env_name = 'Walker2DBulletEnv-v0'
num_cpu = 2 # 学習に使用するCPU数
learn_timesteps = 10**5 # 学習タイムステップ
ori_env = gym.make(env_name)
#env = DummyVecEnv([lambda: ori_env])
env = SubprocVecEnv([make_env(env_name, i) for i in range(num_cpu)])
env.reset()
#env.render()
#time.sleep(5)
savedir = './stable_baselines/{}/'.format(env_name)
logdir = '{}tensorboard_log/'.format(savedir)
os.makedirs(savedir, exist_ok=True)
starttime = datetime.now().strftime("%Y/%m/%d %H:%M:%S")
# 学習の実行
if train:
model = PPO2(MlpPolicy, env, verbose=1, tensorboard_log=logdir)
model.learn(total_timesteps=learn_timesteps)
model.save('{}ppo2_model'.format(savedir))
endtime = datetime.now().strftime("%Y/%m/%d %H:%M:%S")
if curr_idx == -1:
model = PPO2(MlpLnLstmPolicy,
train_env,
verbose=0,
nminibatches=1,
tensorboard_log="./tensorboard",
**model_params)
else:
model = PPO2.load('./agents/ppo2_' + reward_strategy + '_' +
str(curr_idx) + '.pkl',
env=train_env)
for idx in range(curr_idx + 1, 10):
print('[', idx, '] Training for: ', train_len, ' time steps')
model.learn(total_timesteps=train_len)
obs = test_env.reset()
done, reward_sum = False, 0
while not np.all(done):
action, _states = model.predict(obs)
obs, reward, done, info = test_env.step(action)
reward_sum += sum(
[i for i in reward if isinstance(i, int) or isinstance(i, float)])
print('[', idx, '] Total reward: ', reward_sum,
' (' + reward_strategy + ')')
model.save('./agents/ppo2_' + reward_strategy + '_' + str(idx) + '.pkl')
