def f(dist_inputs, values, returns, actions, old_log_probs, mask):
"""Definition of the Proximal Policy Optimization loss."""
del mask # TODO(lukaszkaiser): make PPO work with Transformer
ppo_objective = rl_layers.PPOObjective(
dist_inputs,
stop_gradient(values),
returns,
actions,
old_log_probs,
log_prob_fun=self._policy_dist.log_prob,
epsilon=self._epsilon,
normalize_advantages=self._normalize_advantages)
entropy_loss = rl_layers.EntropyLoss(
dist_inputs,
actions,
log_prob_fun=self._policy_dist.log_prob,
entropy_coeff=self._entropy_coeff,
entropy_fun=self._policy_dist.entropy)
l2_value_loss = rl_layers.ValueLoss(
values, returns, value_loss_coeff=self._value_loss_coeff)
return -ppo_objective.mean() + l2_value_loss - entropy_loss
def f(dist_inputs, values, returns, actions, old_log_probs, mask):
"""Definition of the Proximal Policy Optimization loss."""
del mask # TODO(lukaszkaiser): make PPO work with Transformer
# We have dist_inputs of the shape float32[128,1,18]
assert len(dist_inputs.shape) == 3, (
f'dist_inputs.shape was {dist_inputs.shape}'
f'but expected length of the tensor shape is 3')
# values of the shape float32[128,1,1]
# returns of the shape float32[128,1,1]
# and old_log_probs of the shape float32[128,1]
assert values.shape == returns.shape, (
f'values.shape was {values.shape}'
f'returns.shape was {returns.shape}')
assert returns.shape[0:2] == old_log_probs.shape, (
f'returns.shape was {returns.shape}'
f'old_log_probs.shape was {old_log_probs.shape}')
# actions is a tensor of the shape int32[128,1]
assert len(
actions.shape) == 2, f'actions.shape was {actions.shape}'
# which agrees with returns/values on the first two coordinates
assert actions.shape[0:2] == returns.shape[0:2], (
f'actions.shape was {actions.shape} and'
f'returns.shape was {returns.shape}')
ppo_objective = rl_layers.PPOObjective(
dist_inputs,
stop_gradient(values),
returns,
actions,
old_log_probs,
log_prob_fun=self._policy_dist.log_prob,
epsilon=self._epsilon,
normalize_advantages=self._normalize_advantages)
# we insist that ppo_objective is a vector of shape [128,1]
assert len(ppo_objective.shape) == 2, (
f'ppo_objective was {ppo_objective}')
# which agrees with returns/values/actions on the first two coordinates
assert ppo_objective.shape[0:2] == values.shape[0:2], (
f'ppo_objective.shape was {ppo_objective.shape} and '
f'values.shape was {values.shape}')
entropy_loss = rl_layers.EntropyLoss(
dist_inputs,
actions,
log_prob_fun=self._policy_dist.log_prob,
entropy_coeff=self._entropy_coeff,
entropy_fun=self._policy_dist.entropy)
assert jnp.ndim(
entropy_loss) == 0, f'entropy_loss was {entropy_loss}'
l2_value_loss = rl_layers.ValueLoss(
values, returns, value_loss_coeff=self._value_loss_coeff)
assert jnp.ndim(
l2_value_loss) == 0, f'l2_value_loss was {l2_value_loss}'
return -ppo_objective.mean() + l2_value_loss - entropy_loss
def f(dist_inputs, values, returns, dones, rewards, actions, old_log_probs):
"""Clipped objective from the PPO algorithm."""
ppo_objective = rl_layers.PPOObjective(
dist_inputs, values, returns, dones, rewards, actions, old_log_probs,
log_prob_fun=self._policy_dist.log_prob,
epsilon=self._epsilon,
normalize_advantages=self._normalize_advantages)
return jnp.mean(ppo_objective)
def f(dist_inputs, values, returns, actions, old_log_probs):
return rl_layers.PPOObjective(
dist_inputs,
values,
returns,
actions,
old_log_probs,
log_prob_fun=self._policy_dist.log_prob,
epsilon=self._epsilon,
normalize_advantages=self._normalize_advantages)
def ppo_objective(self):
"""PPO objective with local parameters."""
return tl.Fn(
lambda dist_inputs, values, returns, actions, old_log_probs:
rl_layers.PPOObjective(
dist_inputs, values, returns, actions, old_log_probs,
log_prob_fun=self._policy_dist.log_prob,
epsilon=self._epsilon,
normalize_advantages=self._normalize_advantages),
n_out=1)
