def update_histories(self, coplay):
super().update_histories(coplay)
self._update_scores(coplay)
def meta_strategy(self, results, opponent):
# Choice an action based on the collection of scores
bestscore = max(self.scores)
beststrategies = [
i for (i, score) in enumerate(self.scores) if score == bestscore
]
bestproposals = [results[i] for i in beststrategies]
bestresult = C if C in bestproposals else D
return bestresult
NiceMetaWinner = NiceTransformer()(MetaWinner)
class MetaWinnerEnsemble(MetaWinner):
"""A variant of MetaWinner that chooses one of the top scoring strategies
at random against each opponent. Note this strategy is always stochastic
regardless of the team.
Names:
- Meta Winner Ensemble: Original name by Marc Harper
"""
name = "Meta Winner Ensemble"
def meta_strategy(self, results, opponent):
self._update_scores(opponent)
# Choice an action based on the collection of scores
bestscore = max(self.scores)
beststrategies = [
i for (i, score) in enumerate(self.scores) if score == bestscore
]
bestproposals = [results[i] for i in beststrategies]
bestresult = C if C in bestproposals else D
return bestresult
def reset(self):
super().reset()
self.scores = [0] * len(self.team)
NiceMetaWinner = NiceTransformer()(MetaWinner)
NiceMetaWinner.name = "Nice Meta Winner"
class MetaWinnerEnsemble(MetaWinner):
"""A variant of MetaWinner that chooses one of the top scoring strategies
at random against each opponent. Note this strategy is always stochastic
regardless of the team.
Names:
Meta Winner Ensemble: Original name by Marc Harper
"""
name = "Meta Winner Ensemble"