def test_learning(self):
pol_net = PolNet(self.env.observation_space,
self.env.action_space,
h1=32,
h2=32,
deterministic=True)
noise = OUActionNoise(self.env.action_space)
pol = DeterministicActionNoisePol(self.env.observation_space,
self.env.action_space, pol_net,
noise)
targ_pol_net = PolNet(self.env.observation_space,
self.env.action_space,
32,
32,
deterministic=True)
targ_pol_net.load_state_dict(pol_net.state_dict())
targ_noise = OUActionNoise(self.env.action_space)
targ_pol = DeterministicActionNoisePol(self.env.observation_space,
self.env.action_space,
targ_pol_net, targ_noise)
qf_net = QNet(self.env.observation_space,
self.env.action_space,
h1=32,
h2=32)
qf = DeterministicSAVfunc(self.env.observation_space,
self.env.action_space, qf_net)
targ_qf_net = QNet(self.env.observation_space, self.env.action_space,
32, 32)
targ_qf_net.load_state_dict(targ_qf_net.state_dict())
targ_qf = DeterministicSAVfunc(self.env.observation_space,
self.env.action_space, targ_qf_net)
sampler = EpiSampler(self.env, pol, num_parallel=1)
optim_pol = torch.optim.Adam(pol_net.parameters(), 3e-4)
optim_qf = torch.optim.Adam(qf_net.parameters(), 3e-4)
epis = sampler.sample(pol, max_steps=32)
traj = Traj()
traj.add_epis(epis)
traj = ef.add_next_obs(traj)
traj.register_epis()
result_dict = ddpg.train(traj, pol, targ_pol, qf, targ_qf, optim_pol,
optim_qf, 1, 32, 0.01, 0.9)
del sampler
device_name = 'cpu' if args.cuda < 0 else "cuda:{}".format(args.cuda)
device = torch.device(device_name)
set_device(device)
score_file = os.path.join(args.log, 'progress.csv')
logger.add_tabular_output(score_file)
env = GymEnv(args.env_name, log_dir=os.path.join(
args.log, 'movie'), record_video=args.record)
env.env.seed(args.seed)
ob_space = env.observation_space
ac_space = env.action_space
pol_net = PolNet(ob_space, ac_space, args.h1, args.h2, deterministic=True)
noise = OUActionNoise(ac_space)
pol = DeterministicActionNoisePol(ob_space, ac_space, pol_net, noise)
targ_pol_net = PolNet(ob_space, ac_space, args.h1, args.h2, deterministic=True)
targ_pol_net.load_state_dict(pol_net.state_dict())
targ_noise = OUActionNoise(ac_space.shape)
targ_pol = DeterministicActionNoisePol(
ob_space, ac_space, targ_pol_net, targ_noise)
qf_net = QNet(ob_space, ac_space, args.h1, args.h2)
qf = DeterministicSAVfunc(ob_space, ac_space, qf_net)
targ_qf_net = QNet(ob_space, ac_space, args.h1, args.h2)
targ_qf_net.load_state_dict(qf_net.state_dict())
targ_qf = DeterministicSAVfunc(ob_space, ac_space, targ_qf_net)
record_video=True,
video_schedule=lambda x: True)
env.env.seed(args.seed)
if args.c2d:
env = C2DEnv(env)
observation_space = env.observation_space
action_space = env.action_space
if args.ddpg:
pol_net = PolNet(observation_space,
action_space,
args.pol_h1,
args.pol_h2,
deterministic=True)
noise = OUActionNoise(action_space)
pol = DeterministicActionNoisePol(observation_space, action_space, pol_net,
noise)
else:
if args.rnn:
pol_net = PolNetLSTM(observation_space,
action_space,
h_size=256,
cell_size=256)
else:
pol_net = PolNet(observation_space, action_space)
if isinstance(action_space, gym.spaces.Box):
pol = GaussianPol(observation_space, action_space, pol_net, args.rnn)
elif isinstance(action_space, gym.spaces.Discrete):
pol = CategoricalPol(observation_space, action_space, pol_net,
args.rnn)
log_dir=os.path.join(args.pol_dir, 'movie'),
record_video=args.record)
env.env.seed(args.seed)
if args.c2d:
env = C2DEnv(env)
ob_space = env.observation_space
ac_space = env.action_space
if args.ddpg:
pol_net = PolNet(ob_space,
ac_space,
args.pol_h1,
args.pol_h2,
deterministic=True)
noise = OUActionNoise(ac_space.shape)
pol = DeterministicActionNoisePol(ob_space, ac_space, pol_net, noise)
else:
if args.rnn:
pol_net = PolNetLSTM(ob_space, ac_space, h_size=256, cell_size=256)
else:
pol_net = PolNet(ob_space, ac_space)
if isinstance(ac_space, gym.spaces.Box):
pol = GaussianPol(ob_space, ac_space, pol_net, args.rnn)
elif isinstance(ac_space, gym.spaces.Discrete):
pol = CategoricalPol(ob_space, ac_space, pol_net, args.rnn)
elif isinstance(ac_space, gym.spaces.MultiDiscrete):
pol = MultiCategoricalPol(ob_space, ac_space, pol_net, args.rnn)
else:
raise ValueError('Only Box, Discrete, and MultiDiscrete are supported')
device = torch.device(device_name)
set_device(device)
score_file = os.path.join(args.log, 'progress.csv')
logger.add_tabular_output(score_file)
env = GymEnv(args.env_name, log_dir=os.path.join(
args.log, 'movie'), record_video=args.record)
env.env.seed(args.seed)
observation_space = env.observation_space
action_space = env.action_space
pol_net = PolNet(observation_space, action_space,
args.h1, args.h2, deterministic=True)
noise = OUActionNoise(action_space)
pol = DeterministicActionNoisePol(
observation_space, action_space, pol_net, noise)
targ_pol_net = PolNet(observation_space, action_space,
args.h1, args.h2, deterministic=True)
targ_pol_net.load_state_dict(pol_net.state_dict())
targ_noise = OUActionNoise(action_space)
targ_pol = DeterministicActionNoisePol(
observation_space, action_space, targ_pol_net, targ_noise)
qf_net = QNet(observation_space, action_space, args.h1, args.h2)
qf = DeterministicSAVfunc(observation_space, action_space, qf_net)
targ_qf_net = QNet(observation_space, action_space, args.h1, args.h2)
targ_qf_net.load_state_dict(qf_net.state_dict())