def main():
parser = argparse.ArgumentParser()
parser.add_argument('--env-type', default='gridworld_varibad')
args, rest_args = parser.parse_known_args()
env = args.env_type
# --- GridWorld ---
if env == 'gridworld_belief_oracle':
args = args_grid_belief_oracle.get_args(rest_args)
elif env == 'gridworld_varibad':
args = args_grid_varibad.get_args(rest_args)
elif env == 'gridworld_rl2':
args = args_grid_rl2.get_args(rest_args)
# --- PointRobot 2D Navigation ---
elif env == 'pointrobot_multitask':
args = args_pointrobot_multitask.get_args(rest_args)
elif env == 'pointrobot_varibad':
args = args_pointrobot_varibad.get_args(rest_args)
elif env == 'pointrobot_rl2':
args = args_pointrobot_rl2.get_args(rest_args)
elif env == 'pointrobot_humplik':
args = args_pointrobot_humplik.get_args(rest_args)
# --- MUJOCO ---
# - CheetahDir -
elif env == 'cheetah_dir_multitask':
args = args_cheetah_dir_multitask.get_args(rest_args)
elif env == 'cheetah_dir_expert':
args = args_cheetah_dir_expert.get_args(rest_args)
elif env == 'cheetah_dir_varibad':
args = args_cheetah_dir_varibad.get_args(rest_args)
elif env == 'cheetah_dir_rl2':
args = args_cheetah_dir_rl2.get_args(rest_args)
#
# - CheetahVel -
elif env == 'cheetah_vel_multitask':
args = args_cheetah_vel_multitask.get_args(rest_args)
elif env == 'cheetah_vel_expert':
args = args_cheetah_vel_expert.get_args(rest_args)
elif env == 'cheetah_vel_avg':
args = args_cheetah_vel_avg.get_args(rest_args)
elif env == 'cheetah_vel_varibad':
args = args_cheetah_vel_varibad.get_args(rest_args)
elif env == 'cheetah_vel_rl2':
args = args_cheetah_vel_rl2.get_args(rest_args)
#
# - AntDir -
elif env == 'ant_dir_multitask':
args = args_ant_dir_multitask.get_args(rest_args)
elif env == 'ant_dir_expert':
args = args_ant_dir_expert.get_args(rest_args)
elif env == 'ant_dir_varibad':
args = args_ant_dir_varibad.get_args(rest_args)
elif env == 'ant_dir_rl2':
args = args_ant_dir_rl2.get_args(rest_args)
#
# - AntGoal -
elif env == 'ant_goal_multitask':
args = args_ant_goal_multitask.get_args(rest_args)
elif env == 'ant_goal_expert':
args = args_ant_goal_expert.get_args(rest_args)
elif env == 'ant_goal_varibad':
args = args_ant_goal_varibad.get_args(rest_args)
elif env == 'ant_goal_humplik':
args = args_ant_goal_humplik.get_args(rest_args)
elif env == 'ant_goal_rl2':
args = args_ant_goal_rl2.get_args(rest_args)
#
# - Walker -
elif env == 'walker_multitask':
args = args_walker_multitask.get_args(rest_args)
elif env == 'walker_expert':
args = args_walker_expert.get_args(rest_args)
elif env == 'walker_avg':
args = args_walker_avg.get_args(rest_args)
elif env == 'walker_varibad':
args = args_walker_varibad.get_args(rest_args)
elif env == 'walker_rl2':
args = args_walker_rl2.get_args(rest_args)
#
# - HumanoidDir -
elif env == 'humanoid_dir_multitask':
args = args_humanoid_dir_multitask.get_args(rest_args)
elif env == 'humanoid_dir_expert':
args = args_humanoid_dir_expert.get_args(rest_args)
elif env == 'humanoid_dir_varibad':
args = args_humanoid_dir_varibad.get_args(rest_args)
elif env == 'humanoid_dir_rl2':
args = args_humanoid_dir_rl2.get_args(rest_args)
else:
raise Exception("Invalid Environment")
# warning for deterministic execution
if args.deterministic_execution:
print('Envoking deterministic code execution.')
if torch.backends.cudnn.enabled:
warnings.warn('Running with deterministic CUDNN.')
if args.num_processes > 1:
raise RuntimeError(
'If you want fully deterministic code, run it with num_processes=1.'
'Warning: This will slow things down and might break A2C if '
'policy_num_steps < env._max_episode_steps.')
# if we're normalising the actions, we have to make sure that the env expects actions within [-1, 1]
if args.norm_actions_pre_sampling or args.norm_actions_post_sampling:
envs = make_vec_envs(
env_name=args.env_name,
seed=0,
num_processes=args.num_processes,
gamma=args.policy_gamma,
device='cpu',
episodes_per_task=args.max_rollouts_per_task,
normalise_rew=args.norm_rew_for_policy,
ret_rms=None,
tasks=None,
)
assert np.unique(envs.action_space.low) == [-1]
assert np.unique(envs.action_space.high) == [1]
# clean up arguments
if args.disable_metalearner or args.disable_decoder:
args.decode_reward = False
args.decode_state = False
args.decode_task = False
if hasattr(args, 'decode_only_past') and args.decode_only_past:
args.split_batches_by_elbo = True
# if hasattr(args, 'vae_subsample_decodes') and args.vae_subsample_decodes:
# args.split_batches_by_elbo = True
# begin training (loop through all passed seeds)
seed_list = [args.seed] if isinstance(args.seed, int) else args.seed
for seed in seed_list:
print('training', seed)
args.seed = seed
args.action_space = None
if args.disable_metalearner:
# If `disable_metalearner` is true, the file `learner.py` will be used instead of `metalearner.py`.
# This is a stripped down version without encoder, decoder, stochastic latent variables, etc.
learner = Learner(args)
else:
learner = MetaLearner(args)
learner.train()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--env-type', default='gridworld_varibad')
args, rest_args = parser.parse_known_args()
env = args.env_type
# --- GridWorld ---
# standard
if env == 'gridworld_oracle':
args = args_grid_oracle.get_args(rest_args)
elif env == 'gridworld_belief_oracle':
args = args_grid_belief_oracle.get_args(rest_args)
elif env == 'gridworld_varibad':
args = args_grid_varibad.get_args(rest_args)
elif env == 'gridworld_rl2':
args = args_grid_rl2.get_args(rest_args)
# --- MUJOCO ---
# - AntDir -
elif env == 'mujoco_ant_dir_oracle':
args = args_mujoco_ant_dir_oracle.get_args(rest_args)
elif env == 'mujoco_ant_dir_rl2':
args = args_mujoco_ant_dir_rl2.get_args(rest_args)
elif env == 'mujoco_ant_dir_varibad':
args = args_mujoco_ant_dir_varibad.get_args(rest_args)
#
# - CheetahDir -
elif env == 'mujoco_cheetah_dir_oracle':
args = args_mujoco_cheetah_dir_oracle.get_args(rest_args)
elif env == 'mujoco_cheetah_dir_rl2':
args = args_mujoco_cheetah_dir_rl2.get_args(rest_args)
elif env == 'mujoco_cheetah_dir_varibad':
args = args_mujoco_cheetah_dir_varibad.get_args(rest_args)
#
# - CheetahVel -
elif env == 'mujoco_cheetah_vel_oracle':
args = args_mujoco_cheetah_vel_oracle.get_args(rest_args)
elif env == 'mujoco_cheetah_vel_rl2':
args = args_mujoco_cheetah_vel_rl2.get_args(rest_args)
elif env == 'mujoco_cheetah_vel_varibad':
args = args_mujoco_cheetah_vel_varibad.get_args(rest_args)
#
# - Walker -
elif env == 'mujoco_walker_oracle':
args = args_mujoco_walker_oracle.get_args(rest_args)
elif env == 'mujoco_walker_rl2':
args = args_mujoco_walker_rl2.get_args(rest_args)
elif env == 'mujoco_walker_varibad':
args = args_mujoco_walker_varibad.get_args(rest_args)
#
# - CheetahHField
elif env == 'mujoco_cheetah_hfield_varibad':
args = args_mujoco_cheetah_hfield_varibad.get_args(rest_args)
# - CheetahHill
elif env == 'mujoco_cheetah_hill_varibad':
args = args_mujoco_cheetah_hill_varibad.get_args(rest_args)
# - CheetahBasin
elif env == 'mujoco_cheetah_basin_varibad':
args = args_mujoco_cheetah_basin_varibad.get_args(rest_args)
# - CheetahGentle
elif env == 'mujoco_cheetah_gentle_varibad':
args = args_mujoco_cheetah_gentle_varibad.get_args(rest_args)
# - CheetahSteep
elif env == 'mujoco_cheetah_steep_varibad':
args = args_mujoco_cheetah_steep_varibad.get_args(rest_args)
# - CheetahJoint
elif env == 'mujoco_cheetah_joint_varibad':
args = args_mujoco_cheetah_joint_varibad.get_args(rest_args)
# - CheetahBlocks
elif env == 'mujoco_cheetah_blocks_varibad':
args = args_mujoco_cheetah_blocks_varibad.get_args(rest_args)
# make sure we have log directories for mujoco
if 'mujoco' in env:
try:
os.makedirs(args.agent_log_dir)
except OSError:
files = glob.glob(os.path.join(args.agent_log_dir,
'*.monitor.csv'))
for f in files:
os.remove(f)
eval_log_dir = args.agent_log_dir + "_eval"
try:
os.makedirs(eval_log_dir)
except OSError:
files = glob.glob(os.path.join(eval_log_dir, '*.monitor.csv'))
for f in files:
os.remove(f)
# warning
if args.deterministic_execution:
print('Envoking deterministic code execution.')
if torch.backends.cudnn.enabled:
warnings.warn('Running with deterministic CUDNN.')
if args.num_processes > 1:
raise RuntimeError(
'If you want fully deterministic code, run it with num_processes=1.'
'Warning: This will slow things down and might break A2C if '
'policy_num_steps < env._max_episode_steps.')
# start training
if args.disable_varibad:
# When the flag `disable_varibad` is activated, the file `learner.py` will be used instead of `metalearner.py`.
# This is a stripped down version without encoder, decoder, stochastic latent variables, etc.
learner = Learner(args)
else:
learner = MetaLearner(args)
learner.train()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--env-type', default='ant_dir_rl2')
args, rest_args = parser.parse_known_args()
env = args.env_type
# --- GridWorld ---
if env == 'gridworld_oracle':
args = args_grid_oracle.get_args(rest_args)
elif env == 'gridworld_belief_oracle':
args = args_grid_belief_oracle.get_args(rest_args)
elif env == 'gridworld_varibad':
args = args_grid_varibad.get_args(rest_args)
elif env == 'gridworld_rl2':
args = args_grid_rl2.get_args(rest_args)
# --- MUJOCO ---
# - AntDir -
elif env == 'ant_dir_oracle':
args = args_ant_dir_oracle.get_args(rest_args)
elif env == 'ant_dir_rl2':
args = args_ant_dir_rl2.get_args(rest_args)
elif env == 'ant_dir_varibad':
args = args_ant_dir_varibad.get_args(rest_args)
#
# - AntGoal -
elif env == 'ant_goal_oracle':
args = args_ant_goal_oracle.get_args(rest_args)
elif env == 'ant_goal_varibad':
args = args_ant_goal_varibad.get_args(rest_args)
elif env == 'ant_goal_rl2':
args = args_ant_goal_rl2.get_args(rest_args)
#
# - CheetahDir -
elif env == 'cheetah_dir_oracle':
args = args_cheetah_dir_oracle.get_args(rest_args)
elif env == 'cheetah_dir_rl2':
args = args_cheetah_dir_rl2.get_args(rest_args)
elif env == 'cheetah_dir_varibad':
args = args_cheetah_dir_varibad.get_args(rest_args)
#
# - CheetahVel -
elif env == 'cheetah_vel_oracle':
args = args_cheetah_vel_oracle.get_args(rest_args)
elif env == 'cheetah_vel_rl2':
args = args_cheetah_vel_rl2.get_args(rest_args)
elif env == 'cheetah_vel_varibad':
args = args_cheetah_vel_varibad.get_args(rest_args)
elif env == 'cheetah_vel_avg':
args = args_cheetah_vel_avg.get_args(rest_args)
#
# - Walker -
elif env == 'walker_oracle':
args = args_walker_oracle.get_args(rest_args)
elif env == 'walker_avg':
args = args_walker_avg.get_args(rest_args)
elif env == 'walker_rl2':
args = args_walker_rl2.get_args(rest_args)
elif env == 'walker_varibad':
args = args_walker_varibad.get_args(rest_args)
# warning for deterministic execution
if args.deterministic_execution:
print('Envoking deterministic code execution.')
if torch.backends.cudnn.enabled:
warnings.warn('Running with deterministic CUDNN.')
if args.num_processes > 1:
raise RuntimeError('If you want fully deterministic code, use num_processes 1.'
'Warning: This will slow things down and might break A2C if '
'policy_num_steps < env._max_episode_steps.')
# clean up arguments
if hasattr(args, 'disable_decoder') and args.disable_decoder:
args.decode_reward = False
args.decode_state = False
args.decode_task = False
if hasattr(args, 'decode_only_past') and args.decode_only_past:
args.split_batches_by_elbo = True
# if hasattr(args, 'vae_subsample_decodes') and args.vae_subsample_decodes:
# args.split_batches_by_elbo = True
# begin training (loop through all passed seeds)
seed_list = [args.seed] if isinstance(args.seed, int) else args.seed
for seed in seed_list:
print('training', seed)
args.seed = seed
if args.disable_metalearner:
# If `disable_metalearner` is true, the file `learner.py` will be used instead of `metalearner.py`.
# This is a stripped down version without encoder, decoder, stochastic latent variables, etc.
learner = Learner(args)
else:
learner = MetaLearner(args)
learner.train()