Environment package¶
Environment module:
MAB,MarkovianMAB,ChangingAtEachRepMAB,IncreasingMAB,PieceWiseStationaryMAB,NonStationaryMABobjects, used to wrap the problems (essentially a list of arms).ResultandResultMultiPlayersobjects, used to wrap simulation results (list of decisions and rewards).Evaluatorenvironment, used to wrap simulation, for the single player case.EvaluatorMultiPlayersenvironment, used to wrap simulation, for the multi-players case.EvaluatorSparseMultiPlayersenvironment, used to wrap simulation, for the multi-players case with sparse activated players.CollisionModelsimplements different collision models.
And useful constants and functions for the plotting and stuff:
DPI,signature(),maximizeWindow(),palette(),makemarkers(),wraptext(): for plotting,notify(): send a desktop notification,Parallel(),delayed(): joblib related,tqdm: pretty range() loops,sortedDistance,fairnessMeasures: science related,getCurrentMemory(),sizeof_fmt(): to measure and pretty print memory consumption.
Submodules¶
- Environment.CollisionModels module
- Environment.Evaluator module
- Environment.EvaluatorMultiPlayers module
- Environment.EvaluatorSparseMultiPlayers module
- Environment.MAB module
- Environment.MAB_rotting module
- Environment.Result module
- Environment.ResultMultiPlayers module
- Environment.fairnessMeasures module
- Environment.memory_consumption module
- Environment.notify module
- Environment.plot_Cmu_HOI module
- Environment.plotsettings module
- Environment.pykov module
- Environment.sortedDistance module
- Environment.usejoblib module
- Environment.usenumba module
- Environment.usetqdm module