PoliciesMultiPlayers.rhoCentralized module

rhoCentralized: implementation of the multi-player policy from [Distributed Algorithms for Learning…, Anandkumar et al., 2010](http://ieeexplore.ieee.org/document/5462144/).

• Each child player is selfish, and plays according to an index policy (any index policy, e.g., UCB, Thompson, KL-UCB, BayesUCB etc),

• But instead of aiming at the best (the 1-st best) arm, player i aims at the rank_i-th best arm,

• Every player has rank_i = i + 1, as given by the base station.

Note

This is not fully decentralized: as each child player needs to know the (fixed) number of players, and an initial orthogonal configuration.

Warning

This policy is NOT efficient at ALL! Don’t use it! It seems a smart idea, but it’s not.

class PoliciesMultiPlayers.rhoCentralized.oneRhoCentralized(maxRank, mother, playerId, rank=None, *args, **kwargs)

Bases: PoliciesMultiPlayers.ChildPointer.ChildPointer

Class that acts as a child policy, but in fact it pass all its method calls to the mother class, who passes it to its i-th player.

• The player does not aim at the best arm, but at the rank-th best arm, based on her index policy.

__init__(maxRank, mother, playerId, rank=None, *args, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

maxRank = None

Max rank, usually nbPlayers but can be different

keep_the_same_rank = None

If True, the rank is kept constant during the game, as if it was given by the Base Station

rank = None

Current rank, starting to 1 by default, or ‘rank’ if given as an argument

__str__()

Return str(self).

startGame()

Start game.

handleCollision(arm, reward=None)

Get a new fully random rank, and give reward to the algorithm if not None.

choice()

Chose with the actual rank.

__module__ = 'PoliciesMultiPlayers.rhoCentralized'

class PoliciesMultiPlayers.rhoCentralized.rhoCentralized(nbPlayers, nbArms, playerAlgo, maxRank=None, orthogonalRanks=True, *args, **kwargs)

Bases: PoliciesMultiPlayers.BaseMPPolicy.BaseMPPolicy

rhoCentralized: implementation of a variant of the multi-player rhoRand policy from [Distributed Algorithms for Learning…, Anandkumar et al., 2010](http://ieeexplore.ieee.org/document/5462144/).

__init__(nbPlayers, nbArms, playerAlgo, maxRank=None, orthogonalRanks=True, *args, **kwargs)

• nbPlayers: number of players to create (in self._players).

• playerAlgo: class to use for every players.

• nbArms: number of arms, given as first argument to playerAlgo.

• maxRank: maximum rank allowed by the rhoCentralized child (default to nbPlayers, but for instance if there is 2 × rhoCentralized[UCB] + 2 × rhoCentralized[klUCB], maxRank should be 4 not 2).

• orthogonalRanks: if True, orthogonal ranks 1..M are directly affected to the players 1..M.

• *args, **kwargs: arguments, named arguments, given to playerAlgo.

Example:

>>> from Policies import *
>>> import random; random.seed(0); import numpy as np; np.random.seed(0)
>>> nbArms = 17
>>> nbPlayers = 6
>>> s = rhoCentralized(nbPlayers, nbArms, UCB)
>>> [ child.choice() for child in s.children ]
[12, 15, 0, 3, 3, 7]
>>> [ child.choice() for child in s.children ]
[9, 4, 6, 12, 1, 6]

• To get a list of usable players, use s.children.

• Warning: s._players is for internal use ONLY!

maxRank = None

Max rank, usually nbPlayers but can be different

nbPlayers = None

Number of players

orthogonalRanks = None

Using orthogonal ranks from starting

children = None

List of children, fake algorithms

nbArms = None

Number of arms

__str__()

Return str(self).

__module__ = 'PoliciesMultiPlayers.rhoCentralized'