garage.tf.algos.te_npo
¶
Natural Policy Optimization with Task Embeddings.
- class TENPO(env_spec, policy, baseline, sampler, scope=None, discount=0.99, gae_lambda=1, center_adv=True, positive_adv=False, fixed_horizon=False, lr_clip_range=0.01, max_kl_step=0.01, optimizer=None, optimizer_args=None, policy_ent_coeff=0.0, encoder_ent_coeff=0.0, use_softplus_entropy=False, stop_ce_gradient=False, inference=None, inference_optimizer=None, inference_optimizer_args=None, inference_ce_coeff=0.0, name='NPOTaskEmbedding')¶
Bases:
garage.np.algos.RLAlgorithm
Natural Policy Optimization with Task Embeddings.
See https://karolhausman.github.io/pdf/hausman17nips-ws2.pdf for algorithm reference.
- Parameters
env_spec (EnvSpec) – Environment specification.
policy (garage.tf.policies.TaskEmbeddingPolicy) – Policy.
baseline (garage.tf.baselines.Baseline) – The baseline.
sampler (garage.sampler.Sampler) – Sampler.
scope (str) – Scope for identifying the algorithm. Must be specified if running multiple algorithms simultaneously, each using different environments and policies.
discount (float) – Discount.
gae_lambda (float) – Lambda used for generalized advantage estimation.
center_adv (bool) – Whether to rescale the advantages so that they have mean 0 and standard deviation 1.
positive_adv (bool) – Whether to shift the advantages so that they are always positive. When used in conjunction with center_adv the advantages will be standardized before shifting.
fixed_horizon (bool) – Whether to fix horizon.
lr_clip_range (float) – The limit on the likelihood ratio between policies, as in PPO.
max_kl_step (float) – The maximum KL divergence between old and new policies, as in TRPO.
optimizer (object) – The optimizer of the algorithm. Should be the optimizers in garage.tf.optimizers.
optimizer_args (dict) – The arguments of the optimizer.
policy_ent_coeff (float) – The coefficient of the policy entropy. Setting it to zero would mean no entropy regularization.
encoder_ent_coeff (float) – The coefficient of the policy encoder entropy. Setting it to zero would mean no entropy regularization.
use_softplus_entropy (bool) – Whether to estimate the softmax distribution of the entropy to prevent the entropy from being negative.
stop_ce_gradient (bool) – Whether to stop the cross entropy gradient.
inference (garage.tf.embeddings.StochasticEncoder) – A encoder that infers the task embedding from state trajectory.
inference_optimizer (object) – The optimizer of the inference. Should be an optimizer in garage.tf.optimizers.
inference_optimizer_args (dict) – The arguments of the inference optimizer.
inference_ce_coeff (float) – The coefficient of the cross entropy of task embeddings inferred from task one-hot and state trajectory. This is effectively the coefficient of log-prob of inference.
name (str) – The name of the algorithm.
- train(trainer)¶
Obtain samplers and start actual training for each epoch.
- classmethod get_encoder_spec(task_space, latent_dim)¶
Get the embedding spec of the encoder.
- Parameters
task_space (akro.Space) – Task spec.
latent_dim (int) – Latent dimension.
- Returns
Encoder spec.
- Return type
- classmethod get_infer_spec(env_spec, latent_dim, inference_window_size)¶
Get the embedding spec of the inference.
Every inference_window_size timesteps in the trajectory will be used as the inference network input.
- Parameters
- Returns
Inference spec.
- Return type