def train():
"""Trains an ACER policy """
env = create_env()
model = ACER(policy=CnnPolicy,
env=env,
gamma=0.99,
n_steps=20,
num_procs=4,
q_coef=0.5,
ent_coef=0.01,
max_grad_norm=10,
learning_rate=0.0007,
lr_schedule='linear',
rprop_alpha=0.99,
rprop_epsilon=1e-05,
buffer_size=5000,
replay_ratio=4,
replay_start=1000,
correction_term=10.0,
trust_region=True,
alpha=0.99,
delta=1,
verbose=1,
tensorboard_log="./tb")
model.learn(total_timesteps=int(1e7),
callback=callback,
tb_log_name="acer")
model.save("models/pacman_acer.pkl")
def train_ACER(env_train, model_name, timesteps=25000):
start = time.time()
model = ACER('MlpPolicy', env_train, verbose=0)
model.learn(total_timesteps=timesteps)
end = time.time()
model.save(f"{config.TRAINED_MODEL_DIR}/{model_name}")
print('Training time (A2C): ', (end - start) / 60, ' minutes')
return model
def train_acer(timesteps, name):
env = datares_roulette
env = DummyVecEnv([env])
model = ACER(
stable_baselines.common.policies.MlpPolicy,
env,
verbose=1,
)
model.learn(total_timesteps=timesteps)
model.save(name)
return model
from stable_baselines.common.cmd_util import make_atari_env
from stable_baselines.common.vec_env import VecFrameStack
from stable_baselines import ACER
# There already exists an environment generator
# that will make and wrap atari environments correctly.
# Here we are also multiprocessing training (num_env=4 => 4 processes)
env = make_atari_env('PongNoFrameskip-v4', num_env=4, seed=0)
# Frame-stacking with 4 frames
env = VecFrameStack(env, n_stack=4)
model = ACER('CnnPolicy', env, verbose=1)
model.learn(total_timesteps=25000)
# save
model.save("cnn_pong")
obs = env.reset()
while True:
action, _states = model.predict(obs)
obs, rewards, dones, info = env.step(action)
env.render()
class ACERAgent(Agent):
def __init__(
self,
model_name="model_name",
save_dir="./models",
log_interval=1e4,
num_cpus=8,
eval_episodes=1000,
n_steps=1e6,
layer_normalization=False,
model_kwargs={"tensorboard_log": "./tensorboards/"},
env_kwargs={
"board_size": 4,
"binary": True,
"extractor": "cnn"
},
callback_checkpoint_kwargs={
"save_freq": 0,
"save_path": "./models/",
"name_prefix": "model_name"
},
callback_hist_kwargs={"hist_freq": 0},
):
super().__init__(
model_name,
save_dir,
num_cpus,
model_kwargs,
env_kwargs,
layer_normalization,
callback_checkpoint_kwargs,
callback_hist_kwargs,
n_steps,
log_interval,
eval_episodes,
)
self._init_model()
def _init_model(self):
if not self._model_kwargs["agent"].lower() == "acer":
raise ValueError(
"The model_kwargs dict has to be created using args from ACER agent as reference. Make sure the correct parameters models."
)
del self._model_kwargs["agent"]
self._callback_checkpoint_kwargs["save_freq"] = int(
self._callback_checkpoint_kwargs["save_freq"] / self._num_cpus)
if self._env_kwargs["extractor"] == "mlp":
self._model = ACER(CustomMlpPolicy, self._env,
**self._model_kwargs)
else:
self._model = ACER(CustomCnnPolicy, self._env,
**self._model_kwargs)
def train(self):
"Optimize the model."
callbacks = []
# Checkpoint callback
if self._callback_checkpoint_kwargs["save_freq"] > 0:
# Append model name into checkpoint save_path
self._callback_checkpoint_kwargs["save_path"] = (
self._callback_checkpoint_kwargs["save_path"] + "/" +
str(self._model_name))
checkpoint_callback = CheckpointCallback(
**self._callback_checkpoint_kwargs)
callbacks.append(checkpoint_callback)
if self._callback_hist_kwargs["hist_freq"] > 0:
# hist_callback = CustomCallbackPPO2(**self._callback_hist_kwargs)
# callbacks.append(hist_callback)
pass
try:
self._model.learn(self._n_steps,
log_interval=self._log_interval,
callback=callbacks,
tb_log_name=self._model_name)
except KeyboardInterrupt:
pass
folder_path = os.path.join(self._save_dir, self._model_name)
self._model.save(os.path.join(folder_path, self._model_name))
def test(self):
"Evaluate the model."
mean_reward = super()._test(self._model)
return mean_reward
import gym
from stable_baselines import ACER
from stable_baselines.common.policies import CnnPolicy
from stable_baselines.common.vec_env import DummyVecEnv
# trying to get an idea of how quickly my computer can train this
pong_env = gym.make('Pong-v0')
pong_env = DummyVecEnv([lambda: pong_env])
pong_model_acer = ACER(
CnnPolicy,
pong_env,
verbose=0,
tensorboard_log="./../../data/baselines-stuff/pong/acer_pong_tensorboard/")
pong_model_acer.learn(total_timesteps=50_000_000,
tb_log_name="run-1-50_000_000")
# since I know I'll be stopping it early
pong_model_acer.save(
'./../../data/baselines-stuff/pong/terrible_pong_model_acer')
env = make_vec_env(RPiLEDEnv, env_kwargs=envArgsDict)
callback_on_best = StopTrainingOnRewardThreshold(reward_threshold=-20,
verbose=1)
eval_callback = EvalCallback(env,
best_model_save_path='./logs/best',
log_path='./logs/',
eval_freq=5000,
deterministic=True,
render=False,
callback_on_new_best=callback_on_best)
# Added checkpoint because I lost model data after a crash when the webcam shutdown because the screen went to sleep :(
checkpoint_callback = CheckpointCallback(save_freq=1000,
save_path='./logs/',
name_prefix='ppo1_model')
cb = CallbackList([checkpoint_callback, eval_callback])
policy_kwargs = {'layers': [128]}
model = ACER(MlpLnLstmPolicy,
env,
verbose=1,
policy_kwargs=policy_kwargs,
tensorboard_log='./logs/')
model.learn(total_timesteps=10000, callback=cb)
model.save('acer_rpi_lid')
print('model saved')
env_id = "/home/jim/projects/unity_ray/basic_env_linux/basic_env_linux"
#env = UnityEnv(env_id, worker_id=2, use_visual=False)
# Create log dir
time_int = int(time.time())
log_dir = "stable_results/basic_env_{}/".format(time_int)
os.makedirs(log_dir, exist_ok=True)
#env = DummyVecEnv([lambda: env]) # The algorithms require a vectorized environment to run
num_env = 2
worker_id = 9
env = SubprocVecEnv(
[make_env(env_id, log_dir, i + worker_id) for i in range(num_env)])
model = ACER(MlpPolicy, env, verbose=1)
model.learn(total_timesteps=30000)
model.save(log_dir + "model")
#evaluate agent
episodes = 100
ep_r = []
ep_l = []
for e in range(episodes):
obs = env.reset()
total_r = 0.
total_l = 0.
while True:
action, _states = model.predict(obs)
obs, rewards, dones, infos = env.step(action)
total_l += 1.
total_r += rewards[0]
if dones[0]:
from pathlib import Path
from freqtrade.configuration import Configuration
config = Configuration.from_files(['config_rl.json'])
from freqtradegym import TradingEnv
from stable_baselines.common.policies import MlpPolicy
from stable_baselines import ACER
if __name__ == "__main__":
env = TradingEnv(config)
policy_kwargs = dict(layers=[32, 32])
model = ACER(MlpPolicy,
env,
learning_rate=1e-4,
policy_kwargs=policy_kwargs,
verbose=0,
tensorboard_log="./tensorboard/")
model.learn(total_timesteps=int(1e+6))
model.save('model')