garage.tf.misc.tensor_utils module

center_advs(advs, axes, eps, offset=0, scale=1, name=None)

Normalize the advs tensor

compile_function(inputs, outputs, log_name=None)

compute_advantages(discount, gae_lambda, max_len, baselines, rewards, name=None)

concat_tensor_dict_list(tensor_dict_list)

concat_tensor_list(tensor_list)

discounted_returns(discount, max_len, rewards, name=None)

filter_valids(t, valid, name='filter_valids')

filter_valids_dict(d, valid, name=None)

flatten_batch(t, name='flatten_batch')

flatten_batch_dict(d, name=None)

flatten_inputs(deep)

flatten_tensor_variables(ts)

get_target_ops(variables, target_variables, tau=None)

Get target variables update operations.

In RL algorithms we often update target network every n steps. This function returns the tf.Operation for updating target variables (denoted by target_var) from variables (denote by var) with fraction tau. In other words, each time we want to keep tau of the var and add (1 - tau) of target_var to var.

Parameters:

• variables (list[tf.Variable]) – Soure variables for update.
• target_variable (list[tf.Variable]) – Target variables to be updated.
• tau (float) – Fraction to update. Set it to be None for hard-update.

graph_inputs(name, **kwargs)

new_tensor(name, ndim, dtype)

new_tensor_like(name, arr_like)

pad_tensor(x, max_len)

pad_tensor_dict(tensor_dict, max_len)

pad_tensor_n(xs, max_len)

positive_advs(advs, eps, name=None)

Make all the values in the advs tensor positive

split_tensor_dict_list(tensor_dict)

stack_tensor_dict_list(tensor_dict_list)

Stack a list of dictionaries of {tensors or dictionary of tensors}. :param tensor_dict_list: a list of dictionaries of {tensors or dictionary

of tensors}.

Returns:a dictionary of {stacked tensors or dictionary of stacked tensors}

stack_tensor_list(tensor_list)

unflatten_tensor_variables(flatarr, shapes, symb_arrs)