def launch(args):
env = make_env(args.env_name, env_id=args.env_id,
discrete=True, reward_type=args.reward_type)
# set random seeds for reproducibility
env.seed(args.seed + MPI.COMM_WORLD.Get_rank())
random.seed(args.seed + MPI.COMM_WORLD.Get_rank())
np.random.seed(args.seed + MPI.COMM_WORLD.Get_rank())
torch.manual_seed(args.seed + MPI.COMM_WORLD.Get_rank())
if args.deterministic:
env.make_deterministic()
if args.debug:
logger.set_level(DEBUG)
controller = get_controller(
args.env_name, env_id=args.env_id, discrete=True, num_expansions=args.num_expansions, reward_type=args.reward_type)
# Configure logger
if MPI.COMM_WORLD.Get_rank() == 0 and args.log_dir:
logger.configure(dir=os.path.join(
'logs', 'rts', args.log_dir), format_strs=['tensorboard', 'log', 'csv', 'json', 'stdout'])
args.log_dir = logger.get_dir()
assert args.log_dir is not None
os.makedirs(args.log_dir, exist_ok=True)
env_params = get_env_params(env)
rts_trainer = dqn_rts_agent(args, env, env_params, controller)
rts_trainer.learn()
def launch(args):
assert not args.env_name.startswith(
'Residual'), 'Residual envs not allowed'
# create the ddpg_agent
env = make_env(args.env_name)
controller = get_controller(args.env_name)
# set random seeds for reproducibility
env.seed(args.seed + MPI.COMM_WORLD.Get_rank())
random.seed(args.seed + MPI.COMM_WORLD.Get_rank())
np.random.seed(args.seed + MPI.COMM_WORLD.Get_rank())
torch.manual_seed(args.seed + MPI.COMM_WORLD.Get_rank())
if args.cuda:
torch.cuda.manual_seed(args.seed + MPI.COMM_WORLD.Get_rank())
# Configure logger
if MPI.COMM_WORLD.Get_rank() == 0:
if args.log_dir or logger.get_dir() is None:
logger.configure(
dir=os.path.join('logs', 'switch_her', args.log_dir),
format_strs=['tensorboard', 'log', 'csv', 'json', 'stdout'])
else:
logger.configure(
dir=os.path.join('logs', 'switch_her', args.env_name),
format_strs=['tensorboard', 'log', 'csv', 'json', 'stdout'])
args.log_dir = logger.get_dir()
assert args.log_dir is not None
os.makedirs(args.log_dir, exist_ok=True)
# TODO: Write code for loading and saving params from/to json files
# get the environment parameters
env_params = get_env_params(env)
# create the ddpg agent to interact with the environment
her_trainer = her_switch_agent(args, env, env_params, controller)
her_trainer.learn()
def launch(args):
assert args.env_name.startswith('Residual'), 'Only residual envs allowed'
env = make_env(args.env_name)
# set random seeds for reproducibility
env.seed(args.seed + MPI.COMM_WORLD.Get_rank())
random.seed(args.seed + MPI.COMM_WORLD.Get_rank())
np.random.seed(args.seed + MPI.COMM_WORLD.Get_rank())
# Configure logger
if MPI.COMM_WORLD.Get_rank() == 0:
if args.log_dir or logger.get_dir() is None:
logger.configure(dir=os.path.join(
'logs', 'hardcoded', args.log_dir), format_strs=['tensorboard', 'log', 'csv', 'json', 'stdout'])
else:
logger.configure(dir=os.path.join('logs', 'hardcoded', args.env_name), format_strs=[
'tensorboard', 'log', 'csv', 'json', 'stdout'])
args.log_dir = logger.get_dir()
assert args.log_dir is not None
os.makedirs(args.log_dir, exist_ok=True)
env_params = get_env_params(env)
hardcoded_controller = hardcoded_agent(args, env, env_params)
hardcoded_controller.eval_agent()
def launch(args):
# create the ddpg_agent
env = make_env(args.env_name, env_id=args.env_id,
reward_type=args.reward_type)
# set random seeds for reproducibility
env.seed(args.seed + MPI.COMM_WORLD.Get_rank())
random.seed(args.seed + MPI.COMM_WORLD.Get_rank())
np.random.seed(args.seed + MPI.COMM_WORLD.Get_rank())
torch.manual_seed(args.seed + MPI.COMM_WORLD.Get_rank())
if args.cuda:
torch.cuda.manual_seed(args.seed + MPI.COMM_WORLD.Get_rank())
# Configure logger
if MPI.COMM_WORLD.Get_rank() == 0 and args.log_dir:
logger.configure(dir=os.path.join(
'logs', 'her', args.log_dir), format_strs=['tensorboard', 'log', 'csv', 'json', 'stdout'])
args.log_dir = logger.get_dir()
assert args.log_dir is not None
os.makedirs(args.log_dir, exist_ok=True)
# TODO: Write code for loading and saving params from/to json files
# get the environment parameters
env_params = get_env_params(env)
# create the ddpg agent to interact with the environment
her_trainer = her_agent(args, env, env_params)
her_trainer.learn()
def launch(args):
# rospy.init_node('rts_trainer', anonymous=True)
# Start ray
ray.init(logging_level=logging.ERROR)
# Create environments
env = make_env(env_name=args.env_name,
env_id=args.env_id,
discrete=True,
reward_type=args.reward_type)
planning_env = make_env(env_name=args.env_name,
env_id=args.planning_env_id,
discrete=True,
reward_type=args.reward_type)
# Set random seeds
env.seed(args.seed)
planning_env.seed(args.seed)
# Set global seeds
set_global_seed(args.seed)
# Make deterministic, if you have to
if args.deterministic:
env.make_deterministic()
planning_env.make_deterministic()
# Set logger level to debug, if you have to
if args.debug:
logger.set_level(logger.DEBUG)
# Create controller
controller = get_controller(env_name=args.env_name,
env_id=args.planning_env_id,
discrete=True,
num_expansions=args.n_expansions,
reward_type=args.reward_type,
seed=args.seed)
# Configure logger
if args.log_dir:
logger.configure(
dir=osp.join('logs', 'rts', args.log_dir),
format_strs=['tensorboard', 'log', 'csv', 'json', 'stdout'])
os.makedirs(logger.get_dir(), exist_ok=True)
# Configure save dir
# if args.save_dir:
# args.save_dir = osp.join('saved', 'rts', args.save_dir)
# os.makedirs(args.save_dir, exist_ok=True)
# if args.load_dir:
# args.load_dir = osp.join('saved', 'rts', args.load_dir)
# # TODO: CHeck if dir exists
# Get env params
env_params = get_env_params(args, env)
# Get agent
if args.agent == 'rts' or args.agent == 'mbpo' or args.agent == 'mbpo_knn' or args.agent == 'mbpo_gp':
fetch_trainer = fetch_rts_agent(args, env_params, env, planning_env,
controller)
elif args.agent == 'dqn':
fetch_trainer = fetch_dqn_agent(args, env_params, env, controller)
# elif args.agent == 'mbpo':
# fetch_trainer = fetch_model_agent(args,
# env_params,
# env,
# planning_env,
# controller)
# Start
if args.offline:
# Train in simulation
raise Exception('Only online mode is required')
fetch_trainer.learn_offline_in_model()
else:
n_steps = fetch_trainer.learn_online_in_real_world(args.max_timesteps)
print('REACHED GOAL in', n_steps, 'by agent', args.agent)
ray.shutdown()
time.sleep(5)
return n_steps