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ppo: add entropy cost annealing (linear + cosine schedules) #660

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ppo: add entropy cost annealing (linear + cosine schedules) #660

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1 change: 1 addition & 0 deletions brax/training/agents/ppo/losses.py
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Original file line number Diff line number Diff line change
Expand Up @@ -213,6 +213,7 @@ def compute_ppo_loss(
'policy_loss': policy_loss,
'v_loss': v_loss,
'entropy_loss': entropy_loss,
'entropy_cost': entropy_cost,
'kl_mean': kl,
'policy_dist_mean_std': policy_dist_mean_std,
}
37 changes: 33 additions & 4 deletions brax/training/agents/ppo/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -212,6 +212,8 @@ def train(
# ppo params
learning_rate: float = 1e-4,
entropy_cost: float = 1e-4,
entropy_cost_end: Optional[float] = None,
entropy_schedule: str = 'linear',
discounting: float = 0.9,
unroll_length: int = 10,
batch_size: int = 32,
Expand Down Expand Up @@ -464,10 +466,22 @@ def reset_fn_donated_env_state(env_state_donated, key_envs):
else:
optimizer = base_optimizer

# Entropy annealing helpers.
def _uint64_to_jnp(u):
return jnp.float32(u.hi) * 4294967296.0 + jnp.float32(u.lo)

def _compute_entropy_cost(env_steps, total_steps, start, end, schedule):
progress = jnp.clip(
_uint64_to_jnp(env_steps) / float(total_steps), 0.0, 1.0
)
if schedule == 'cosine':
return end + (start - end) * (1.0 + jnp.cos(jnp.pi * progress)) / 2.0
else: # linear
return start + (end - start) * progress

loss_fn = functools.partial(
ppo_losses.compute_ppo_loss,
ppo_network=ppo_network,
entropy_cost=entropy_cost,
discounting=discounting,
reward_scaling=reward_scaling,
gae_lambda=gae_lambda,
Expand All @@ -491,11 +505,13 @@ def minibatch_step(
carry,
data: types.Transition,
normalizer_params: running_statistics.RunningStatisticsState,
entropy_cost: jnp.ndarray = None,
):
optimizer_state, params, key = carry
key, key_loss = jax.random.split(key)
(_, metrics), grads = loss_and_pgrad_fn(
params, normalizer_params, data, key_loss
params, normalizer_params, data, key_loss,
entropy_cost=entropy_cost,
)

if lr_is_adaptive_kl:
Expand All @@ -519,6 +535,7 @@ def sgd_step(
unused_t,
data: types.Transition,
normalizer_params: running_statistics.RunningStatisticsState,
entropy_cost: jnp.ndarray = None,
):
optimizer_state, params, key = carry
key, key_perm, key_grad = jax.random.split(key, 3)
Expand All @@ -542,7 +559,11 @@ def convert_data(x: jnp.ndarray):

shuffled_data = jax.tree_util.tree_map(convert_data, data)
(optimizer_state, params, _), metrics = jax.lax.scan(
functools.partial(minibatch_step, normalizer_params=normalizer_params),
functools.partial(
minibatch_step,
normalizer_params=normalizer_params,
entropy_cost=entropy_cost,
),
(optimizer_state, params, key_grad),
shuffled_data,
length=num_minibatches,
Expand Down Expand Up @@ -612,9 +633,17 @@ def f(carry, unused_t):
pmap_axis_name=_PMAP_AXIS_NAME,
)

if entropy_cost_end is not None:
current_entropy_cost = _compute_entropy_cost(
training_state.env_steps, num_timesteps,
entropy_cost, entropy_cost_end, entropy_schedule)
else:
current_entropy_cost = jnp.array(entropy_cost, dtype=jnp.float32)

(optimizer_state, params, _), metrics = jax.lax.scan(
functools.partial(
sgd_step, data=data, normalizer_params=normalizer_params
sgd_step, data=data, normalizer_params=normalizer_params,
entropy_cost=current_entropy_cost,
),
(training_state.optimizer_state, training_state.params, key_sgd),
(),
Expand Down