tutorials.examples.train_bitsequence_recurrent
==============================================

.. py:module:: tutorials.examples.train_bitsequence_recurrent

.. autoapi-nested-parse::

   Minimal TB training on BitSequence with a recurrent policy.

   Key choices:
   - RecurrentDiscretePolicyEstimator + RecurrentDiscreteSequenceModel
   - Sampler uses RecurrentEstimatorAdapter (saves on-policy log-probs)
   - TBGFlowNet with constant_pb=True (tree DAG), pb=None

   This is intentionally small and mirrors train_hypergrid_simple.py structure.



Attributes
----------

.. autoapisummary::

   tutorials.examples.train_bitsequence_recurrent.parser


Functions
---------

.. autoapisummary::

   tutorials.examples.train_bitsequence_recurrent.estimated_dist
   tutorials.examples.train_bitsequence_recurrent.main


Module Contents
---------------

.. py:function:: estimated_dist(gflownet, env)

.. py:function:: main(args)

.. py:data:: parser