agent = DDPG(nb_states, nb_actions, args)
evaluate = Evaluator(args.validate_episodes,
args.validate_steps, args.output, max_episode_length=args.max_episode_length)
exp = None
if args.mode == 'train':
exp = Experiment("sim2real-ddpg-real-cheetah")
for arg in ["env", "rate", "prate", "hidden1", "hidden2", "warmup", "discount",
"bsize", "rmsize", "window_length", "tau", "ou_theta", "ou_sigma", "ou_mu",
"validate_episodes", "max_episode_length", "validate_steps", "init_w",
"train_iter", "epsilon", "seed", "resume"]:
arg_val = getattr(args, arg)
import socket
exp.param("host", socket.gethostname())
train(args, args.train_iter, agent, env, evaluate,
args.validate_steps, args.output, max_episode_length=args.max_episode_length, debug=args.debug, exp=exp)
# when done
exp.end()
elif args.mode == 'test':
test(args.validate_episodes, agent, env, evaluate, args.resume,
visualize=True, debug=args.debug)
else:
raise RuntimeError('undefined mode {}'.format(args.mode))
def run_pusher3dof(args, sim=True, vanilla=False):
try:
from hyperdash import Experiment
hyperdash_support = True
except:
hyperdash_support = False
env = NormalizedEnv(gym.make(args.env))
torques = [1.0] * 3 # if real
colored = False
if sim:
torques = [args.t0, args.t1, args.t2]
colored = True
if not vanilla:
env.env._init(
torques=torques,
colored=colored
)
if args.seed > 0:
np.random.seed(args.seed)
env.seed(args.seed)
nb_states = env.observation_space.shape[0]
nb_actions = env.action_space.shape[0]
agent = DDPG(nb_states, nb_actions, args)
evaluate = Evaluator(
args.validate_episodes,
args.validate_steps,
args.output,
max_episode_length=args.max_episode_length
)
exp = None
if args.mode == 'train':
if hyperdash_support:
prefix = "real"
if sim: prefix = "sim"
exp = Experiment("s2r-pusher3dof-ddpg-{}".format(prefix))
import socket
exp.param("host", socket.gethostname())
exp.param("type", prefix) # sim or real
exp.param("vanilla", vanilla) # vanilla or not
exp.param("torques", torques)
exp.param("folder", args.output)
for arg in ["env", "max_episode_length", "train_iter", "seed", "resume"]:
arg_val = getattr(args, arg)
exp.param(arg, arg_val)
train(args, args.train_iter, agent, env, evaluate,
args.validate_steps, args.output,
max_episode_length=args.max_episode_length, debug=args.debug, exp=exp)
# when done
exp.end()
elif args.mode == 'test':
test(args.validate_episodes, agent, env, evaluate, args.resume,
visualize=args.vis, debug=args.debug, load_best=args.best)
else:
raise RuntimeError('undefined mode {}'.format(args.mode))
def run_reacher(args, sim=True):
try:
from hyperdash import Experiment
hyperdash_support = True
except:
hyperdash_support = False
env = NormalizedEnv(gym.make(args.env))
torques = [200, 200] # if real
colors = None
if sim:
torques = [args.t0, args.t1]
colors = {
"arenaBackground": ".27 .27 .81",
"arenaBorders": "1.0 0.8 0.4",
"arm0": "0.9 0.6 0.9",
"arm1": "0.9 0.9 0.6"
}
env.env.env._init( # real robot
torque0=torques[0], # torque of joint 1
torque1=torques[0], # torque of joint 2
topDown=True,
colors=colors)
if args.seed > 0:
np.random.seed(args.seed)
env.seed(args.seed)
nb_states = env.observation_space.shape[0]
nb_actions = env.action_space.shape[0]
agent = DDPG(nb_states, nb_actions, args)
evaluate = Evaluator(args.validate_episodes,
args.validate_steps,
args.output,
max_episode_length=args.max_episode_length)
exp = None
if args.mode == 'train':
if hyperdash_support:
prefix = "real"
if sim: prefix = "sim"
exp = Experiment("s2r-reacher-ddpg-{}".format(prefix))
import socket
exp.param("host", socket.gethostname())
exp.param("type", prefix) # sim or real
exp.param("torques", [torques[0], torques[1]])
exp.param("folder", args.output)
for arg in [
"env", "max_episode_length", "train_iter", "seed", "resume"
]:
arg_val = getattr(args, arg)
exp.param(arg, arg_val)
train(args,
args.train_iter,
agent,
env,
evaluate,
args.validate_steps,
args.output,
max_episode_length=args.max_episode_length,
debug=args.debug,
exp=exp)
# when done
exp.end()
elif args.mode == 'test':
test(args.validate_episodes,
agent,
env,
evaluate,
args.resume,
visualize=args.vis,
debug=args.debug,
load_best=args.best)
else:
raise RuntimeError('undefined mode {}'.format(args.mode))