def __init__(self, params):
#############
## INIT
#############
# Get params, create logger
self.params = params
self.logger = Logger(self.params["logdir"])
# Set random seeds
seed = self.params["seed"]
np.random.seed(seed)
torch.manual_seed(seed)
ptu.init_gpu(use_gpu=not self.params["no_gpu"],
gpu_id=self.params["which_gpu"])
#############
## ENV
#############
# Make the gym environment
register_custom_envs()
self.env = gym.make(self.params["env_name"])
self.eval_env = gym.make(self.params["env_name"])
if not ("pointmass" in self.params["env_name"]):
import matplotlib
matplotlib.use("Agg")
self.env.set_logdir(self.params["logdir"] + "/expl_")
self.eval_env.set_logdir(self.params["logdir"] + "/eval_")
if "env_wrappers" in self.params:
# These operations are currently only for Atari envs
self.env = wrappers.Monitor(self.env,
os.path.join(self.params["logdir"],
"gym"),
force=True)
self.eval_env = wrappers.Monitor(self.eval_env,
os.path.join(
self.params["logdir"], "gym"),
force=True)
self.env = params["env_wrappers"](self.env)
self.eval_env = params["env_wrappers"](self.eval_env)
self.mean_episode_reward = -float("nan")
self.best_mean_episode_reward = -float("inf")
if "non_atari_colab_env" in self.params and self.params[
"video_log_freq"] > 0:
self.env = wrappers.Monitor(
self.env,
os.path.join(self.params["logdir"], "gym"),
write_upon_reset=True,
) # , force=True)
self.eval_env = wrappers.Monitor(
self.eval_env,
os.path.join(self.params["logdir"], "gym"),
write_upon_reset=True,
)
self.mean_episode_reward = -float("nan")
self.best_mean_episode_reward = -float("inf")
self.env.seed(seed)
self.eval_env.seed(seed)
# Maximum length for episodes
self.params["ep_len"] = self.params[
"ep_len"] or self.env.spec.max_episode_steps
global MAX_VIDEO_LEN
MAX_VIDEO_LEN = self.params["ep_len"]
# Is this env continuous, or self.discrete?
discrete = isinstance(self.env.action_space, gym.spaces.Discrete)
# Are the observations images?
img = len(self.env.observation_space.shape) > 2
self.params["agent_params"]["discrete"] = discrete
# Observation and action sizes
ob_dim = (self.env.observation_space.shape
if img else self.env.observation_space.shape[0])
ac_dim = self.env.action_space.n if discrete else self.env.action_space.shape[
0]
self.params["agent_params"]["ac_dim"] = ac_dim
self.params["agent_params"]["ob_dim"] = ob_dim
# simulation timestep, will be used for video saving
if "model" in dir(self.env):
self.fps = 1 / self.env.model.opt.timestep
elif "env_wrappers" in self.params:
self.fps = 30 # This is not actually used when using the Monitor wrapper
elif "video.frames_per_second" in self.env.env.metadata.keys():
self.fps = self.env.env.metadata["video.frames_per_second"]
else:
self.fps = 10
#############
## AGENT
#############
agent_class = self.params["agent_class"]
self.agent = agent_class(self.env, self.params["agent_params"])
def __init__(self, params):
#############
## INIT
#############
# Get params, create logger
self.params = params
self.logger = Logger(self.params['logdir'])
# Set random seeds
seed = self.params['seed']
np.random.seed(seed)
tf.random.set_seed(seed)
#############
## ENV
#############
# Make the gym environment
register_custom_envs()
self.env = gym.make(self.params['env_name'])
self.eval_env = gym.make(self.params['env_name'])
if not ('pointmass' in self.params['env_name']):
import matplotlib
matplotlib.use('Agg')
self.env.set_logdir(self.params['logdir'] + '/expl_')
self.eval_env.set_logdir(self.params['logdir'] + '/eval_')
if 'env_wrappers' in self.params:
# These operations are currently only for Atari envs
self.env = wrappers.Monitor(self.env,
os.path.join(self.params['logdir'],
"gym"),
force=True)
self.eval_env = wrappers.Monitor(self.eval_env,
os.path.join(
self.params['logdir'], "gym"),
force=True)
self.env = params['env_wrappers'](self.env)
self.eval_env = params['env_wrappers'](self.eval_env)
self.mean_episode_reward = -float('nan')
self.best_mean_episode_reward = -float('inf')
if 'non_atari_colab_env' in self.params and self.params[
'video_log_freq'] > 0:
self.env = wrappers.Monitor(self.env,
os.path.join(self.params['logdir'],
"gym"),
write_upon_reset=True) #, force=True)
self.eval_env = wrappers.Monitor(self.eval_env,
os.path.join(
self.params['logdir'], "gym"),
write_upon_reset=True)
self.mean_episode_reward = -float('nan')
self.best_mean_episode_reward = -float('inf')
self.env.seed(seed)
self.eval_env.seed(seed)
# Maximum length for episodes
self.params['ep_len'] = self.params[
'ep_len'] or self.env.spec.max_episode_steps
global MAX_VIDEO_LEN
MAX_VIDEO_LEN = self.params['ep_len']
# Is this env continuous, or self.discrete?
discrete = isinstance(self.env.action_space, gym.spaces.Discrete)
# Are the observations images?
img = len(self.env.observation_space.shape) > 2
self.params['agent_params']['discrete'] = discrete
# Observation and action sizes
ob_dim = self.env.observation_space.shape if img else self.env.observation_space.shape[
0]
ac_dim = self.env.action_space.n if discrete else self.env.action_space.shape[
0]
self.params['agent_params']['ac_dim'] = ac_dim
self.params['agent_params']['ob_dim'] = ob_dim
# simulation timestep, will be used for video saving
if 'model' in dir(self.env):
self.fps = 1 / self.env.model.opt.timestep
elif 'env_wrappers' in self.params:
self.fps = 30 # This is not actually used when using the Monitor wrapper
elif 'video.frames_per_second' in self.env.env.metadata.keys():
self.fps = self.env.env.metadata['video.frames_per_second']
else:
self.fps = 10
#############
## AGENT
#############
agent_class = self.params['agent_class']
self.agent = agent_class(self.env, self.params['agent_params'])
def __init__(self, params):
#############
## INIT
#############
# Get params, create logger
self.params = params
self.logger = Logger(self.params['logdir'])
# Set random seeds
seed = self.params['seed']
np.random.seed(seed)
torch.manual_seed(seed)
ptu.init_gpu(
use_gpu=not self.params['no_gpu'],
gpu_id=self.params['which_gpu']
)
#############
## ENV
#############
# Make the gym environment
register_custom_envs()
self.env = gym.make(self.params['env_name'])
if 'env_wrappers' in self.params:
# These operations are currently only for Atari envs
# <<<<<<< HEAD
# self.env = wrappers.Monitor(self.env, os.path.join(self.params['logdir'], "gym"), force=True)
# # self.env.enabled = (self.params['video_log_freq'] > 0)
# =======
self.env = wrappers.Monitor(
self.env,
os.path.join(self.params['logdir'], "gym"),
force=True,
video_callable=(None if self.params['video_log_freq'] > 0 else False),
)
# >>>>>>> b5d34989d30c72b353acc2a64e691d17ddbea81f
self.env = params['env_wrappers'](self.env)
self.mean_episode_reward = -float('nan')
self.best_mean_episode_reward = -float('inf')
if 'non_atari_colab_env' in self.params and self.params['video_log_freq'] > 0:
self.env = wrappers.Monitor(
self.env,
os.path.join(self.params['logdir'], "gym"),
force=True,
video_callable=(None if self.params['video_log_freq'] > 0 else False),
)
self.mean_episode_reward = -float('nan')
self.best_mean_episode_reward = -float('inf')
self.env.seed(seed)
# import plotting (locally if 'obstacles' env)
if not(self.params['env_name']=='obstacles-cs285-v0'):
import matplotlib
matplotlib.use('Agg')
# Maximum length for episodes
self.params['ep_len'] = self.params['ep_len'] or self.env.spec.max_episode_steps
global MAX_VIDEO_LEN
MAX_VIDEO_LEN = self.params['ep_len']
# Is this env continuous, or self.discrete?
discrete = isinstance(self.env.action_space, gym.spaces.Discrete)
# Are the observations images?
img = len(self.env.observation_space.shape) > 2
self.params['agent_params']['discrete'] = discrete
# Observation and action sizes
ob_dim = self.env.observation_space.shape if img else self.env.observation_space.shape[0]
ac_dim = self.env.action_space.n if discrete else self.env.action_space.shape[0]
self.params['agent_params']['ac_dim'] = ac_dim
self.params['agent_params']['ob_dim'] = ob_dim
# simulation timestep, will be used for video saving
if 'model' in dir(self.env):
self.fps = 1/self.env.model.opt.timestep
elif 'env_wrappers' in self.params:
self.fps = 30 # This is not actually used when using the Monitor wrapper
elif 'video.frames_per_second' in self.env.env.metadata.keys():
self.fps = self.env.env.metadata['video.frames_per_second']
else:
self.fps = 10
#############
## AGENT
#############
agent_class = self.params['agent_class']
if 'hparam' in self.params['exp_name']:
print('changing optimizer')
self.params['agent_params']['optimizer_spec'] = lander_optimizer(self.params['lr'])
print('using lr = ', self.params['lr'])
self.agent = agent_class(self.env, self.params['agent_params'])
else:
self.agent = agent_class(self.env, self.params['agent_params'])
