AllMixedStrategyRewards = []
AllMixedStrategyActions = []
# MixedStrategyRewards = np.zeros(variables['NumAgents'])
CorrelatedEquilRewards = np.zeros(variables["NumAgents"])
# MixedStrategyActions = np.zeros(variables['NumAgents'])
CorrelatedEquilActions = np.zeros(variables["NumAgents"])
C = np.zeros((2, 2))
for i in [0, 1]:
for j in [0, 1]:
C[i, j] = environment.calculatecapacity(Agents[i], BSs[j], 1, variables, doFading=False)
for strat in analysis_helper.findPossibleStrategies(C, variables["K_coexistence"]):
AllMixedStrategyRewards.append(
[
analysis_helper.calcUtilityFromStrategy(0, strat, variables["K_coexistence"], C),
analysis_helper.calcUtilityFromStrategy(1, strat, variables["K_coexistence"], C),
]
)
AllMixedStrategyActions.append([strat[0][1], strat[1][1]])
# bestmixed = analysis_helper.findBestMixedStrategy(C, variables["K_coexistence"])
# MixedStrategyRewards = MixedStrategyRewards + [analysis_helper.calcUtilityFromStrategy(0, bestmixed, variables["K_coexistence"], C), analysis_helper.calcUtilityFromStrategy(1, bestmixed, variables["K_coexistence"], C)]
# MixedStrategyActions = MixedStrategyActions + [bestmixed[0][1], bestmixed[1][1]]
foundcorre, corstrat, corrrewardsloc = analysis_helper.calc_correlated_equil(C, variables["K_coexistence"])
# print(foundcorre, corstrat, corrrewardsloc)
if foundcorre:
CorrelatedEquilRewards = CorrelatedEquilRewards + corrrewardsloc
CorrelatedEquilActions[0] = CorrelatedEquilActions[0] + 1 - corstrat
CorrelatedEquilActions[1] = CorrelatedEquilActions[1] + corstrat
logsumaverageRewards[configindex][index] = math.log(epsilon +np.mean(AgentRewards[0][-100:-1]), 2) + math.log(epsilon + np.mean(AgentRewards[1][-100:-1]), 2)
if configindex is 0:
foundcorre = False
AllMixedStrategyRewards = []
CorrelatedEquilRewards = np.zeros(variables['NumAgents'])
CorrelatedEquilRewards_constrained = np.zeros(variables['NumAgents'])
C = np.zeros((2, 2))
for i in [0, 1]:
for j in [0, 1]:
C[i,j] = environment.calculatecapacity(Agents[i], BSs[j], 1, variables, doFading=False)
stratsmixed = analysis_helper.findALLPossibleStrategies(C, variables["K_coexistence"])
for j in range(0, len(stratsmixed)):
u1 = epsilon + analysis_helper.calcUtilityFromStrategy(0, stratsmixed[j], variables["K_coexistence"], C)
u2 = epsilon + analysis_helper.calcUtilityFromStrategy(1, stratsmixed[j], variables["K_coexistence"], C)
logval = math.log(u1, 2) + math.log(u2, 2)
logsumaverageRewards_MixedStrategies[j][index] = logval
logsumaverageRewards_MixedMax[index] = max(logsumaverageRewards_MixedMax[index], logval)
foundcorre, corstrat, corrrewardsloc = analysis_helper.calc_correlated_equil_without_constraint(C, variables["K_coexistence"])
if foundcorre:
CorrelatedEquilRewards = corrrewardsloc
logsumaverageRewards_Correlated[index] = math.log(epsilon +CorrelatedEquilRewards[0], 2) + math.log(epsilon + CorrelatedEquilRewards[1], 2)
foundcorre, corstrat, corrrewardsloc = analysis_helper.calc_correlated_equil(C, variables["K_coexistence"])
if foundcorre:
CorrelatedEquilRewards_constrained = corrrewardsloc
logsumaverageRewards_CorrelatedConstrained[index] = math.log(epsilon +CorrelatedEquilRewards_constrained[0], 2) + math.log(epsilon + CorrelatedEquilRewards[1], 2)
print(output_file)
