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utils.py
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utils.py
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import gym
from gym import spaces
import numpy as np
import os
from stable_baselines.results_plotter import load_results, ts2xy
from stable_baselines.common.callbacks import BaseCallback
class SaveOnBestTrainingRewardCallback(BaseCallback):
"""
Callback for saving a model (the check is done every ``check_freq`` steps)
based on the training reward (in practice, we recommend using ``EvalCallback``).
:param check_freq: (int)
:param log_dir: (str) Path to the folder where the model will be saved.
It must contains the file created by the ``Monitor`` wrapper.
:param verbose: (int)
"""
def __init__(self, check_freq: int, log_dir: str, verbose=1):
super(SaveOnBestTrainingRewardCallback, self).__init__(verbose)
self.check_freq = check_freq
self.log_dir = log_dir
self.save_path = os.path.join(log_dir, 'best_model')
self.best_mean_reward = -np.inf
def _init_callback(self) -> None:
# Create folder if needed
if self.save_path is not None:
os.makedirs(self.save_path, exist_ok=True)
def _on_step(self) -> bool:
if self.n_calls % self.check_freq == 0:
# Retrieve training reward
x, y = ts2xy(load_results(self.log_dir), 'timesteps')
if len(x) > 0:
# Mean training reward over the last 100 episodes
mean_reward = np.mean(y[-100:])
if self.verbose > 0:
print("Num timesteps: {}".format(self.num_timesteps))
print(
"Best mean reward: {:.2f} - Last mean reward per episode: {:.2f}"
.format(self.best_mean_reward, mean_reward))
# New best model, you could save the agent here
if mean_reward > self.best_mean_reward:
self.best_mean_reward = mean_reward
# Example for saving best model
if self.verbose > 0:
print("Saving new best model to {}".format(
self.save_path))
self.model.save(self.save_path)
return True
class SaveOnBestTrainingRewardCallbackCustom(BaseCallback):
"""
Callback for saving a model (the check is done every ``check_freq`` steps)
based on the training reward (in practice, we recommend using ``EvalCallback``).
:param check_freq: (int)
:param log_dir: (str) Path to the folder where the model will be saved.
It must contains the file created by the ``Monitor`` wrapper.
:param verbose: (int)
"""
def __init__(self, check_freq: int, log_dir: str, verbose=1):
super(SaveOnBestTrainingRewardCallbackCustom, self).__init__(verbose)
self.check_freq = check_freq
self.log_dir = log_dir
self.save_path = os.path.join(log_dir, 'best_model')
self.best_mean_reward = -np.inf
self.auto_saves_timesteps = [
100_000, 150_000, 200_000, 250_000, 400_000, 500_000, 750_000,
1_000_000, 1_500_000, 2_000_00, 2_500_000, 3_000_000, 3_500_000,
4_000_000, 5_000_000, 7_000_000, 10_000_000
]
self.auto_saves_timesteps.sort() # to avoid mistakes
def _init_callback(self) -> None:
# Create folder if needed
if self.save_path is not None:
os.makedirs(self.save_path, exist_ok=True)
def _on_step(self) -> bool:
if self.n_calls % self.check_freq == 0:
# Retrieve training reward
x, y = ts2xy(load_results(self.log_dir), 'timesteps')
if len(x) > 0:
# Mean training reward over the last 100 episodes
mean_reward = np.mean(y[-100:])
std_reward = np.std(y[-100:])
if self.verbose > 0:
print("Num timesteps: {}".format(self.num_timesteps))
print(
"Best mean reward: {:.2f} - Last mean reward per episode: {:.2f} +- {:.2f}"
.format(self.best_mean_reward, mean_reward,
std_reward))
# New best model, you could save the agent here
if mean_reward > self.best_mean_reward:
self.best_mean_reward = mean_reward
# Example for saving best model
if self.verbose > 0:
print("Saving new best model to {}".format(
self.save_path))
self.model.save(self.save_path)
#periodic save for later
if self.auto_saves_timesteps and self.num_timesteps>=self.auto_saves_timesteps[0]:
periodic_save_path=os.path.join(self.log_dir, 'model_{}'.format(self.auto_saves_timesteps[0]))
if self.verbose > 0:
print("Saving periodic model - {}".format(
periodic_save_path))
self.model.save(periodic_save_path)
del self.auto_saves_timesteps[0]
return True
class TimeLimitWrapper(gym.Wrapper):
"""
:param env: (gym.Env) Gym environment that will be wrapped
:param max_steps: (int) Max number of steps per episode
"""
def __init__(self, env, max_steps=100):
# Call the parent constructor, so we can access self.env later
super(TimeLimitWrapper, self).__init__(env)
self.max_steps = max_steps
# Counter of steps per episode
self.current_step = 0
def reset(self):
"""
Reset the environment
"""
# Reset the counter
self.current_step = 0
return self.env.reset()
def step(self, action):
"""
:param action: ([float] or int) Action taken by the agent
:return: (np.ndarray, float, bool, dict) observation, reward, is the episode over?, additional informations
"""
self.current_step += 1
obs, reward, done, info = self.env.step(action)
# Overwrite the done signal when
if self.current_step >= self.max_steps:
done = True
# Update the info dict to signal that the limit was exceeded
info['time_limit_reached'] = True
return obs, reward, done, info
#Baselines common wrappers
class TimeLimit(gym.Wrapper):
def __init__(self, env, max_episode_steps=None):
super(TimeLimit, self).__init__(env)
self._max_episode_steps = max_episode_steps
self._elapsed_steps = 0
def step(self, ac):
observation, reward, done, info = self.env.step(ac)
self._elapsed_steps += 1
if self._elapsed_steps >= self._max_episode_steps:
done = True
info['TimeLimit.truncated'] = True
return observation, reward, done, info
def reset(self, **kwargs):
self._elapsed_steps = 0
return self.env.reset(**kwargs)
class ClipActionsWrapper(gym.Wrapper):
def step(self, action):
import numpy as np
action = np.nan_to_num(action)
action = np.clip(action, self.action_space.low, self.action_space.high)
return self.env.step(action)
def reset(self, **kwargs):
return self.env.reset(**kwargs)