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)
print(net_i2a)
obs = envs[0].reset()
obs_v = ptan.agent.default_states_preprocessor([obs]).to(device)
res = net_i2a(obs_v)
optimizer = optim.RMSprop(net_i2a.parameters(), lr=LEARNING_RATE, eps=1e-5)
policy_opt = optim.Adam(net_policy.parameters(), lr=POLICY_LR)
step_idx = 0
total_steps = 0
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--cuda", default=False, action="store_true", help="Enable cuda")
parser.add_argument("-n", "--name", required=True, help="Name of the run")
parser.add_argument("-m", "--model", required=True, help="File with model to load")
args = parser.parse_args()
device = torch.device("cuda" if args.cuda else "cpu")
saves_path = os.path.join("saves", "02_env_" + args.name)
os.makedirs(saves_path, exist_ok=True)
envs = [common.make_env() for _ in range(NUM_ENVS)]
writer = SummaryWriter(comment="-02_env_" + args.name)
net = common.AtariA2C(envs[0].observation_space.shape, envs[0].action_space.n)
net_em = i2a.EnvironmentModel(envs[0].observation_space.shape, envs[0].action_space.n).to(device)
net.load_state_dict(torch.load(args.model, map_location=lambda storage, loc: storage))
net = net.to(device)
print(net_em)
optimizer = optim.Adam(net_em.parameters(), lr=LEARNING_RATE)
step_idx = 0
best_loss = np.inf
with ptan.common.utils.TBMeanTracker(writer, batch_size=100) as tb_tracker:
for mb_obs, mb_obs_next, mb_actions, mb_rewards, done_rewards, done_steps in iterate_batches(envs, net, device):
if len(done_rewards) > 0:
m_reward = np.mean(done_rewards)
m_steps = np.mean(done_steps)
print("%d: done %d episodes, mean reward=%.2f, steps=%.2f" % (
step_idx, len(done_rewards), m_reward, m_steps))
tb_tracker.track("total_reward", m_reward, step_idx)
