libmcts
A Monte Carlo Tree Search Library
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mcts::MinSampleSelector< Context, Config > Class Template Reference

selects the child with the lowest number of samples. More...

#include <min_sample_selector.hpp>

Inheritance diagram for mcts::MinSampleSelector< Context, Config >:
mcts::MinFunctionSelector< Context, Config > mcts::ISelectionStrategy< Context, Config >

Public Member Functions

virtual double evaluate (node_t *node) const
 this function has to be implemented in derived classes that specializes the metric to be used. every child is evaluated against this method. More...
 
- Public Member Functions inherited from mcts::MinFunctionSelector< Context, Config >
node_tselect (node_t *node)
 select a child of node More...
 

Additional Inherited Members

- Public Types inherited from mcts::ISelectionStrategy< Context, Config >
typedef ISelectionStrategy< Context, Config > sstrategy_t
 

Detailed Description

template<typename Context, typename Config>
class mcts::MinSampleSelector< Context, Config >

selects the child with the lowest number of samples.

Template Parameters
Context@README
Config@README

Definition at line 15 of file min_sample_selector.hpp.

Member Function Documentation

◆ evaluate()

template<typename Context , typename Config >
virtual double mcts::MinSampleSelector< Context, Config >::evaluate ( node_t node) const
inlinevirtual

this function has to be implemented in derived classes that specializes the metric to be used. every child is evaluated against this method.

Parameters
nodeto evaluate
Returns
number representing some metric.

Implements mcts::MinFunctionSelector< Context, Config >.

Definition at line 21 of file min_sample_selector.hpp.

21 { return node->nb_samples(); }

The documentation for this class was generated from the following file: