config = experiment_config.ExperimentCfg()
config.make_i2a_config(parser)
device = torch.device(config.DEVICE)
writer = SummaryWriter(comment="_i2a_fc_" +
config.build_name_for_i2a_writer())
saves_path = writer.logdir
envs = [
common.makeCustomizedGridEnv(config) for _ in range(config.NUM_ENVS)
]
test_env = common.makeCustomizedGridEnv(config)
#sets seed on torch operations and on all environments
common.set_seed(config.SEED, envs=envs)
common.set_seed(config.SEED, envs=[test_env])
obs_shape = envs[0].observation_space.shape
act_n = envs[0].action_space.n
# net_policy = common.AtariA2C(obs_shape, act_n).to(device)
net_policy = common.getNet(config)
config.A2CNET = str(net_policy)
net_em = models.environment_model.EnvironmentModel(obs_shape, act_n,
config)
# net_em.load_state_dict(torch.load(config.EM_FILE_NAME, map_location=lambda storage, loc: storage))
net_em = net_em.to(device)
config.EM_NET = str(net_em)
parser = argparse.ArgumentParser()
parser.add_argument("-n", "--name", required=True, help="Name of the run")
parser.add_argument("--cuda", default=False, action="store_true", help="Enable CUDA")
parser.add_argument("--em", required=True, help="Environment model file name")
parser.add_argument("--seed", type=int, default=common.DEFAULT_SEED, help="Random seed to use, default=%d" % common.DEFAULT_SEED)
args = parser.parse_args()
device = torch.device("cuda" if args.cuda else "cpu")
saves_path = os.path.join("saves", "03_i2a_" + args.name)
os.makedirs(saves_path, exist_ok=True)
envs = [common.make_env() for _ in range(common.NUM_ENVS)]
test_env = common.make_env(test=True)
if args.seed:
common.set_seed(args.seed, envs, cuda=args.cuda)
suffix = "-seed=%d" % args.seed
else:
suffix = ""
writer = SummaryWriter(comment="-03_i2a_" + args.name + suffix)
obs_shape = envs[0].observation_space.shape
act_n = envs[0].action_space.n
net_policy = common.AtariA2C(obs_shape, act_n).to(device)
net_em = i2a.EnvironmentModel(obs_shape, act_n)
net_em.load_state_dict(torch.load(args.em, map_location=lambda storage, loc: storage))
net_em = net_em.to(device)
net_i2a = i2a.I2A(obs_shape, act_n, net_em, net_policy, ROLLOUTS_STEPS).to(device)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--cuda", default=True, action="store_true", help="Enable cuda")
parser.add_argument("-n", "--name", required=True, help="Name of the run")
parser.add_argument("--seed", type=int, default=common.DEFAULT_SEED, help="Random seed to use, default=%d" % common.DEFAULT_SEED)
parser.add_argument("--steps", type=int, default=None, help="Limit of training steps, default=disabled")
args = parser.parse_args()
device = torch.device("cuda" if args.cuda else "cpu")
print('device: ', device, )
saves_path = os.path.join("saves", "01_a2c_" + args.name)
os.makedirs(saves_path, exist_ok=True)
envs = [common.make_env() for _ in range(common.NUM_ENVS)]
if args.seed:
common.set_seed(args.seed, envs, cuda=args.cuda)
suffix = "-seed=%d" % args.seed
else:
suffix = ""
test_env = common.make_env(test=True)
writer = SummaryWriter(comment="-01_a2c_" + args.name + suffix)
net = common.AtariA2C(envs[0].observation_space.shape, envs[0].action_space.n).to(device)
print(net)
optimizer = optim.RMSprop(net.parameters(), lr=LEARNING_RATE, eps=1e-5)
step_idx = 0
total_steps = 0
best_reward = None
ts_start = time.time()
net = net.to(device)
config.A2CNET = str(net)
# net = common.AtariA2C(envs[0].observation_space.shape, envs[0].action_space.n)
net_em = models.environment_model.EnvironmentModel(
envs[0].observation_space.shape, envs[0].action_space.n,
config).to(device)
# net_em.load_state_dict(torch.load("/home/valy/OneDrive/experiments/repl/9_22/Jan19_20-40-19_valy_em_22_9_True/best_1.4249e-06_195121.dat", map_location=lambda storage, loc: storage))
config.EM_NET = str(net_em)
print(net)
print(net_em)
print("em param count: " + str(common.count_parameters(net_em)))
# sets seed on torch operations and on all environments
common.set_seed(seed=config.SEED, envs=envs)
optimizer = optim.Adam(net_em.parameters(), lr=config.LEARNING_RATE)
epoch = 0
best_loss = np.inf
desc = ""
pbar = trange(config.EM_STEPS, desc='', leave=True)
progress = iter(pbar)
with ptan.common.utils.TBMeanTracker(
writer, batch_size=config.BATCH_SIZE) as tb_tracker:
#obtain batch transitions from the a2c model free agent (st, at, st+1, r)
for mb_obs, mb_obs_next, mb_actions, mb_rewards, done_rewards, done_steps in collect_experience(
envs, net, config, device):
if len(done_rewards) > 0:
