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, dones, rewards, actions,
old_log_probs, mask):
"""Definition of the A2C loss."""
del old_log_probs
# Typically 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]
assert values.shape == returns.shape, (
f'values.shape was {values.shape}'
f'returns.shape was (returns.shape)')
# actions of the shape int32[128,1] in the case of discrete actions
# and float32[128,1,6] in the case of of half-cheetah
# actions agree with returns/values on the first two coordinates
assert actions.shape[0:2] == returns.shape[0:2], (
f'actions.shape was {actions.shape}'
f'returns.shape was (returns.shape)')
# and mask of the shape float32[128,1]
assert len(mask.shape) == 2, f'mask.shape was {mask.shape}'
# which agrees with returns/values/actions on the first two coordinates
assert mask.shape[0:2] == returns.shape[0:2], (
f'mask.shape was {mask.shape}'
f'returns.shape was (returns.shape)')
a2c_objective = rl_layers.A2CObjective(
dist_inputs,
stop_gradient(values),
returns,
dones,
rewards,
actions,
mask,
log_prob_fun=self._policy_dist.log_prob,
normalize_advantages=self._normalize_advantages)
# we insist that a2c_objective is a scalar
assert jnp.ndim(
a2c_objective) == 0, f'a2c_objective was {a2c_objective}'
entropy_loss = rl_layers.EntropyLoss(
dist_inputs,
distribution=self._policy_dist,
coeff=self._entropy_coeff,
)
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}'
combined_loss = a2c_objective + l2_value_loss - entropy_loss
return combined_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, actions, old_log_probs, mask):
"""Definition of the A2C loss."""
del old_log_probs
a2c_objective = rl_layers.A2CObjective(
dist_inputs,
stop_gradient(values),
returns,
actions,
mask,
log_prob_fun=self._policy_dist.log_prob,
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 a2c_objective.mean() + l2_value_loss - entropy_loss
def f(dist_inputs, values, returns): del dist_inputs return rl_layers.ValueLoss(values, returns, self._value_loss_coeff)
def f(dist_inputs, values, returns):
del dist_inputs
return rl_layers.ValueLoss(values, returns, 1)
def value_loss(self):
"""Value loss - so far generic for all A2C."""
return tl.Fn(lambda dist_inputs, values, returns: rl_layers.ValueLoss(
values, returns, self._value_loss_coeff),
n_out=1)
def value_loss(self):
"""Value loss - so far generic for all A2C."""
layer = tl.Fn(lambda dist_inputs, values, returns: rl_layers.ValueLoss(
values, returns, self._value_loss_coeff),
n_in=3, n_out=1)
return lambda **unused_kwargs: layer