def main():
# Command line arguments
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--outfile")
parser.add_argument("--metadata")
parser.add_argument("--plot", type=int, default=0)
parser.add_argument("--just_sim", action="store_true")
# Parameters
parser.add_argument("--n_iter", type=int, default=150)
parser.add_argument("--gamma", type=float, default=.99)
parser.add_argument("--lam", type=float, default=1.0)
parser.add_argument("--timesteps_per_batch", type=int, default=50000)
parser.add_argument("--penalty_coeff", type=float, default=0.5)
parser.add_argument("--max_pathlength", type=int, default=1000)
args = parser.parse_args()
# mdp = mjcmdp.CartpoleMDP()
np.random.seed(args.seed)
mdp = mjcmdp.HopperMDP()
(_, (ctrl_dim, )) = mdp.action_spec()
(_, (obs_dim, )) = mdp.observation_spec()
policy = MujocoPolicy(obs_dim, ctrl_dim)
# Saving to HDF5
hdf, diagnostics = prepare_h5_file(args, {"policy": policy, "mdp": mdp})
vf = MujocoNeuralValueFunction(num_features=38, num_hidden=40)
for (iteration, stats) in enumerate(
ppo.run_ppo(mdp,
policy,
vf=vf,
gamma=args.gamma,
lam=args.lam,
max_pathlength=args.max_pathlength,
timesteps_per_batch=args.timesteps_per_batch,
n_iter=args.n_iter,
parallel=False,
penalty_coeff=args.penalty_coeff)):
std_a = policy.get_stdev()
for (i, s) in enumerate(std_a):
stats["std_%i" % i] = s
print tabulate(stats.items())
for (statname, statval) in stats.items():
diagnostics[statname].append(statval)
if args.plot:
animate_rollout(mdp,
policy,
delay=.001,
horizon=args.max_pathlength)
grp = hdf.create_group("snapshots/%.4i" % (iteration))
policy.pc.to_h5(grp)
def main():
# Command line arguments
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--outfile")
parser.add_argument("--metadata")
parser.add_argument("--plot", type=int, default=0)
parser.add_argument("--just_sim", action="store_true")
# Parameters
parser.add_argument("--n_iter", type=int, default=150)
parser.add_argument("--gamma", type=float, default=0.99)
parser.add_argument("--lam", type=float, default=1.0)
parser.add_argument("--timesteps_per_batch", type=int, default=50000)
parser.add_argument("--penalty_coeff", type=float, default=0.5)
parser.add_argument("--max_pathlength", type=int, default=1000)
args = parser.parse_args()
# mdp = mjcmdp.CartpoleMDP()
np.random.seed(args.seed)
mdp = mjcmdp.HopperMDP()
(_, (ctrl_dim,)) = mdp.action_spec()
(_, (obs_dim,)) = mdp.observation_spec()
policy = MujocoPolicy(obs_dim, ctrl_dim)
# Saving to HDF5
hdf, diagnostics = prepare_h5_file(args, {"policy": policy, "mdp": mdp})
vf = MujocoNeuralValueFunction(num_features=38, num_hidden=40)
for (iteration, stats) in enumerate(
ppo.run_ppo(
mdp,
policy,
vf=vf,
gamma=args.gamma,
lam=args.lam,
max_pathlength=args.max_pathlength,
timesteps_per_batch=args.timesteps_per_batch,
n_iter=args.n_iter,
parallel=False,
penalty_coeff=args.penalty_coeff,
)
):
std_a = policy.get_stdev()
for (i, s) in enumerate(std_a):
stats["std_%i" % i] = s
print tabulate(stats.items())
for (statname, statval) in stats.items():
diagnostics[statname].append(statval)
if args.plot:
animate_rollout(mdp, policy, delay=0.001, horizon=args.max_pathlength)
grp = hdf.create_group("snapshots/%.4i" % (iteration))
policy.pc.to_h5(grp)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--outfile")
parser.add_argument("--metadata")
parser.add_argument("--plot", type=int, default=0)
parser.add_argument("--game",
type=str,
choices=[
"pong", "breakout", "enduro", "beam_rider",
"space_invaders", "seaquest", "qbert"
],
default='pong')
# Parameters
parser.add_argument("--n_iter", type=int, default=1000)
parser.add_argument("--gamma", type=float, default=.98)
parser.add_argument("--lam", type=float, default=1.00)
parser.add_argument("--timesteps_per_batch", type=int, default=30000)
parser.add_argument("--penalty_coeff", type=float, default=0.5)
parser.add_argument("--max_pathlength", type=int, default=10000)
parser.add_argument("--max_kl", type=float, default=.04)
args = parser.parse_args()
np.random.seed(args.seed)
mdp = AtariMDP('atari_roms/%s.bin' % args.game)
policy = AtariRAMPolicy(mdp.n_actions)
vf = AtariRamLinearValueFunction()
hdf, diagnostics = prepare_h5_file(args, {"policy": policy, "mdp": mdp})
for (iteration, stats) in enumerate(
ppo.run_ppo(mdp,
policy,
vf=vf,
gamma=args.gamma,
lam=args.lam,
max_pathlength=args.max_pathlength,
timesteps_per_batch=args.timesteps_per_batch,
n_iter=args.n_iter,
parallel=True,
max_kl=0.04,
penalty_coeff=args.penalty_coeff)):
print tabulate(stats.items())
for (statname, statval) in stats.items():
diagnostics[statname].append(statval)
if args.plot:
animate_rollout(mdp, policy, delay=.001, horizon=100)
grp = hdf.create_group("snapshots/%.4i" % (iteration))
policy.pc.to_h5(grp)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--seed",type=int,default=0)
parser.add_argument("--outfile")
parser.add_argument("--metadata")
parser.add_argument("--plot",type=int,default=0)
parser.add_argument("--game",type=str,choices=["pong","breakout","enduro","beam_rider","space_invaders","seaquest","qbert"],default='pong')
# Parameters
parser.add_argument("--n_iter",type=int,default=1000)
parser.add_argument("--gamma",type=float,default=.98)
parser.add_argument("--lam",type=float,default=1.00)
parser.add_argument("--timesteps_per_batch",type=int,default=30000)
parser.add_argument("--penalty_coeff",type=float,default=0.5)
parser.add_argument("--max_pathlength",type=int,default=10000)
parser.add_argument("--max_kl",type=float,default=.04)
args = parser.parse_args()
np.random.seed(args.seed)
mdp = AtariMDP('atari_roms/%s.bin'%args.game)
policy = AtariRAMPolicy(mdp.n_actions)
vf = AtariRamLinearValueFunction()
hdf, diagnostics = prepare_h5_file(args, {"policy" : policy, "mdp" : mdp})
for (iteration,stats) in enumerate(ppo.run_ppo(
mdp, policy,
vf=vf,
gamma=args.gamma,
lam=args.lam,
max_pathlength = args.max_pathlength,
timesteps_per_batch = args.timesteps_per_batch,
n_iter = args.n_iter,
parallel=True,
max_kl = 0.04,
penalty_coeff=args.penalty_coeff)):
print tabulate(stats.items())
for (statname, statval) in stats.items():
diagnostics[statname].append(statval)
if args.plot:
animate_rollout(mdp,policy,delay=.001,horizon=100)
grp = hdf.create_group("snapshots/%.4i"%(iteration))
policy.pc.to_h5(grp)
