model = Model(ac_space=ac_space,
policy_network=network,
ent_coef=0.0,
vf_coef=0.5,
max_grad_norm=0.5)
ckpt = tf.train.Checkpoint(model=model)
manager = tf.train.CheckpointManager(ckpt, '../models/PPO22', max_to_keep=None)
ckpt.restore(manager.latest_checkpoint)
obs = env.reset()
state = model.initial_state
episode_reward = 0
while True:
if state is not None:
actions, _, state, _ = model.step(obs)
else:
actions, _, _, _ = model.step(obs)
obs, rew, done, _ = env.step(actions.numpy())
episode_reward += rew
env.render()
time.sleep(1 / 24)
if done:
print(f'episode_reward={episode_reward}')
episode_reward = 0
# try:
# action = model(ob)
# print(obs)
# assert len(obs) == 1, len(obs)
# adopted_obs = FootballEnv.convert_observations_static(
# original=obs[0], player=player,
# left_player_position=0,
# right_player_position=0,
# config=config,
# )
# a = self._action_to_list(player.take_action(adopted_obs))
# for k, v in sorted(obs[0].items()): print(k, v)
print(obs.shape)
# actions = player.take_action(observations=obs)
actions, values, states, neglogpacs = model.step(obs,
S=states,
M=dones)
# action, _states = model.predict(obs)
# assert 0, action
assert len(actions) == 1, actions
# while int(actions[0]._backend_action) >= NUM_ACTIONS:
# print(actions)
# actions = player.take_action(observations=obs)
# assert len(actions) == 1, actions
# print(actions)
# ACTION_TO_INDEX_MAP = {a:i for i, a in enumerate(DEFAULT_ACTION_SET)}
# actions = [ACTION_TO_INDEX_MAP[a] for a in actions]
obs, rew, done, _ = env.step(actions)
logging.info(
'Playing the game, step %d, action %s, rew %s, done %d', cnt,
actions, rew, done)