def main():
state_features = StateFeatures()
env = BatchedStateEnv(create_batched_env(NUM_ENVS, augment=True),
state_features=state_features)
model = ACModel()
prior = ACModel()
discriminator = DiscriminatorModel()
if os.path.exists('save.pkl'):
model.load_state_dict(torch.load('save.pkl'))
if os.path.exists('save_disc.pkl'):
discriminator.load_state_dict(torch.load('save_disc.pkl'))
else:
discriminator.cnn.load_state_dict(model.cnn.state_dict())
if os.path.exists('save_prior.pkl'):
prior.load_state_dict(torch.load('save_prior.pkl'))
model.to(torch.device('cuda'))
discriminator.to(torch.device('cuda'))
prior.to(torch.device('cuda'))
train, test = load_data(augment=True)
recordings = train + test
roller = LogRoller(env, model, HORIZON)
ppo = Prierarchy(prior, model, gamma=GAE_GAMMA, lam=GAE_LAM, lr=LR, ent_reg=PRIOR_REG)
gail = GAIL(discriminator, lr=LR)
gail.outer_loop(ppo,
roller,
recordings,
state_features,
rew_scale=GAIL_REWARD_SCALE,
real_rew_scale=REWARD_SCALE,
disc_num_steps=HORIZON * NUM_ENVS // BATCH_SIZE,
disc_batch_size=BATCH_SIZE,
expert_batch=GAIL_NUM_ENVS,
expert_horizon=GAIL_HORIZON,
num_steps=ITERS,
batch_size=BATCH_SIZE)
def main():
args = arg_parser().parse_args()
env = BatchedStateEnv(
create_batched_env(NUM_ENVS,
augment=True,
start=args.worker_idx,
rand_floors=(args.min, args.max)))
model = ACModel()
prior = ACModel()
if os.path.exists(args.path):
model.load_state_dict(torch.load(args.path))
if os.path.exists('save_prior.pkl'):
prior.load_state_dict(torch.load('save_prior.pkl'))
model.to(torch.device('cuda'))
prior.to(torch.device('cuda'))
roller = LogRoller(env, model, HORIZON)
ppo = Prierarchy(prior,
model,
gamma=GAE_GAMMA,
lam=GAE_LAM,
lr=LR,
ent_reg=PRIOR_REG)
ppo.outer_loop(roller,
num_steps=ITERS,
batch_size=BATCH_SIZE,
save_path=args.path)
def main():
env = BatchedStateEnv(create_batched_env(NUM_ENVS))
model = ACModel()
if os.path.exists('save.pkl'):
model.load_state_dict(torch.load('save.pkl'))
model.to(torch.device('cuda'))
roller = Roller(env, model, HORIZON)
ppo = PPO(model, gamma=GAE_GAMMA, lam=GAE_LAM, lr=LR, ent_reg=ENTROPY_REG)
ppo.outer_loop(roller, num_steps=ITERS, batch_size=BATCH_SIZE)