PoliciesMultiPlayers.rhoLearn module¶
rhoLearn: implementation of the multi-player policy from [Distributed Algorithms for Learning…, Anandkumar et al., 2010](http://ieeexplore.ieee.org/document/5462144/), using a learning algorithm instead of a random exploration for choosing the rank.
- 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,
- At first, every player has a random rank_i from 1 to M, and when a collision occurs, rank_i is given by a second learning algorithm, playing on arms = ranks from [1, .., M], where M is the number of player.
- If rankSelection = Uniform, this is like rhoRand, but if it is a smarter policy, it might be better! Warning: no theoretical guarantees exist!
- Reference: [Proof-of-Concept System for Opportunistic Spectrum Access in Multi-user Decentralized Networks, S.J.Darak, C.Moy, J.Palicot, EAI 2016](https://doi.org/10.4108/eai.5-9-2016.151647), algorithm 2. (for BayesUCB only)
Note
This is not fully decentralized: as each child player needs to know the (fixed) number of players.
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PoliciesMultiPlayers.rhoLearn.
CHANGE_RANK_EACH_STEP
= False¶ Should oneRhoLearn players select a (possibly new) rank at each step ? The algorithm P2 from https://doi.org/10.4108/eai.5-9-2016.151647 suggests to do so. But I found it works better without this trick.
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class
PoliciesMultiPlayers.rhoLearn.
oneRhoLearn
(maxRank, rankSelectionAlgo, change_rank_each_step, *args, **kwargs)[source]¶ Bases:
PoliciesMultiPlayers.rhoRand.oneRhoRand
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.
- Except for the handleCollision method: a (possibly new) rank is sampled after observing a collision, from the rankSelection algorithm.
- When no collision is observed on a arm, a small reward is given to the rank used for this play, in order to learn the best ranks with rankSelection.
- And the player does not aim at the best arm, but at the rank-th best arm, based on her index policy.
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__init__
(maxRank, rankSelectionAlgo, change_rank_each_step, *args, **kwargs)[source]¶ Initialize self. See help(type(self)) for accurate signature.
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maxRank
= None¶ Max rank, usually nbPlayers but can be different
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rank
= None¶ Current rank, starting to 1
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change_rank_each_step
= None¶ Change rank at each step?
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getReward
(arm, reward)[source]¶ Give a 1 reward to the rank selection algorithm (no collision), give reward to the arm selection algorithm, and if self.change_rank_each_step, select a (possibly new) rank.
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handleCollision
(arm, reward=None)[source]¶ Give a 0 reward to the rank selection algorithm, and select a (possibly new) rank.
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__module__
= 'PoliciesMultiPlayers.rhoLearn'¶
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class
PoliciesMultiPlayers.rhoLearn.
rhoLearn
(nbPlayers, nbArms, playerAlgo, rankSelectionAlgo=<class 'Policies.Uniform.Uniform'>, lower=0.0, amplitude=1.0, maxRank=None, change_rank_each_step=False, *args, **kwargs)[source]¶ Bases:
PoliciesMultiPlayers.rhoRand.rhoRand
rhoLearn: implementation of the multi-player policy from [Distributed Algorithms for Learning…, Anandkumar et al., 2010](http://ieeexplore.ieee.org/document/5462144/), using a learning algorithm instead of a random exploration for choosing the rank.
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__init__
(nbPlayers, nbArms, playerAlgo, rankSelectionAlgo=<class 'Policies.Uniform.Uniform'>, lower=0.0, amplitude=1.0, maxRank=None, change_rank_each_step=False, *args, **kwargs)[source]¶ - 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.
- rankSelectionAlgo: algorithm to use for selecting the ranks.
- maxRank: maximum rank allowed by the rhoRand child (default to nbPlayers, but for instance if there is 2 × rhoRand[UCB] + 2 × rhoRand[klUCB], maxRank should be 4 not 2).
- *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 >>> stickyTime = 5 >>> s = rhoLearn(nbPlayers, nbArms, UCB, 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!
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maxRank
= None¶ Max rank, usually nbPlayers but can be different
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nbPlayers
= None¶ Number of players
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children
= None¶ List of children, fake algorithms
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rankSelectionAlgo
= None¶ Policy to use to chose the ranks
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nbArms
= None¶ Number of arms
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change_rank_each_step
= None¶ Change rank at every steps?
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__module__
= 'PoliciesMultiPlayers.rhoLearn'¶
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