def _make_schedule(
self,
history,
start_lr=1e-3,
observation_metrics=(("eval", "metrics/accuracy"),),
action_multipliers=(1.0,),
):
policy_and_value_model = atari_cnn.FrameStackMLP
net = ppo.policy_and_value_net(
n_actions=len(action_multipliers),
n_controls=1,
bottom_layers_fn=policy_and_value_model,
two_towers=False,
)
rng = jax_random.get_prng(seed=0)
obs_dim = len(observation_metrics)
(params, state) = net.initialize((1, 1, obs_dim), np.float32, rng)
policy_dir = self.get_temp_dir()
# Optimizer slots should not be used for anything.
slots = None
opt_state = (params, slots)
ppo.save_opt_state(policy_dir, opt_state, state, epoch=0, total_opt_step=0)
return learning_rate.PolicySchedule(
history,
observation_metrics=observation_metrics,
include_lr_in_observation=False,
action_multipliers=action_multipliers,
start_lr=start_lr,
policy_and_value_model=policy_and_value_model,
policy_and_value_two_towers=False,
policy_dir=policy_dir,
)
def test_saves_and_restores_opt_state(self):
opt_state = 123
state = 456
epoch = 7
opt_step = 89
output_dir = self.get_temp_dir()
ppo.save_opt_state(output_dir, opt_state, state, epoch, opt_step)
restored_data = ppo.maybe_restore_opt_state(output_dir)
self.assertEqual(restored_data, (opt_state, state, epoch, opt_step))
def save(self):
"""Save the agent parameters."""
logging.vlog(1, "PPO epoch [% 6d]: saving model.", self._epoch)
ppo.save_opt_state(
self._output_dir,
self._policy_and_value_opt_state,
self._model_state,
self._epoch,
self._total_opt_step,
)
# Reset this number.
self._n_trajectories_done = 0
self._last_saved_at = self._epoch
