def play(list_of_players, rng, v=False):
pathData = os.path.join('data', 'simulations')
game = Games.PrisonersDilemma('Joshua')
action_space = game.actionSpace
L = len(list_of_players)
avgScoreM = np.zeros([L, L])
for i in range(5):
if v:
print(i)
for k in list_of_players:
if k.name[:6] == 'Neural':
k.prepThread(len(list_of_players), 16)
else:
k.clearHistory(len(list_of_players))
game.tournament(list_of_players, 200, rng, True)
for j in range(L):
for k in range(L):
avgScoreM[j, k] += np.sum(
list_of_players[j].lastScore[k, :]) / 200
try:
list_of_players[j].resetState()
except:
pass
#print(avgScoreM/100)
df = {}
#arr = np.array(list_avgScore)
for i in range(avgScoreM.shape[0]):
df[list_of_players[i].name] = avgScoreM[i, :] / 5
df = pd.DataFrame(df)
df.to_csv(os.path.join(pathData, 'Matrix_avgPlayers_rng.csv'))
def stability(nReplicates, epochs, gamma, v=False):
game = Games.PrisonersDilemma('Joshua')
action_space = game.actionSpace
pathData = os.path.join('data', 'simulations')
avgScoreM = np.zeros(epochs * nReplicates)
avg_op = np.zeros(epochs * nReplicates)
avg_me = np.zeros(epochs * nReplicates)
avg_switches_op = np.zeros(epochs * nReplicates)
avg_switches_me = np.zeros(epochs * nReplicates)
f = 0
for i in range(nReplicates):
player1 = strat.Neural203Agent(name='Neural',
actionSpace=game.actionSpace)
player1.gamma = gamma
player3 = strat.TitF2tatAgent(name='TitF2Tat')
list_of_players = [player1, player3]
if v:
print('process id:', os.getpid(), '\tReplicat nr:', i)
for j in range(epochs * i, epochs * i + epochs):
for k in list_of_players:
if k.name == 'Neural':
k.prepThread(len(list_of_players) - 1 + f, 10)
else:
k.clearHistory(len(list_of_players) - 1 + f)
game.tournament(list_of_players, 200, 0.05, False)
avgScoreM[j] = np.sum(list_of_players[0].lastScore) / (200)
avg_op[j] = np.sum(list_of_players[0].lastOp) / (200)
avg_me[j] = np.sum(list_of_players[0].lastMe) / (200)
avg_switches_op[j] = countSwitches(
list_of_players[0].lastOp[0]) / (200)
avg_switches_me[j] = countSwitches(
list_of_players[0].lastMe[0]) / (200)
for j in list_of_players:
if j.name == 'Neural':
j.train()
df = {}
df['avgScore'] = avgScoreM
df['avgOp'] = avg_op
df['avgMe'] = avg_me
df['avgSOp'] = avg_switches_op
df['avgSMe'] = avg_switches_me
df = pd.DataFrame(df)
df.to_csv(os.path.join(pathData, 'Stability_data_gamma{}'.format(gamma)))
def train(list_of_players, rng=0.0, v=False):
pathData = os.path.join('data', 'training')
pathModel = 'model'
game = Games.PrisonersDilemma('Joshua')
action_space = game.actionSpace
list_avgScore = []
noise = 0.2
bestScore = 0
for i in range(1000):
if v:
print(i)
if i % 10 == 0:
noise *= 0.9
noise = np.max([noise, 0.05])
for k in list_of_players:
if k.name[:6] == 'Neural':
k.prepThread(len(list_of_players) - 1, 16)
else:
k.clearHistory(len(list_of_players) - 1)
game.tournament(list_of_players, 200, noise)
avgScore = []
for j in list_of_players:
avgScore.append(
np.sum(j.lastScore) / (200 * (len(list_of_players) - 1)))
if j.name[:6] == 'Neural':
j.train()
try:
j.resetState()
except:
pass
list_avgScore.append(avgScore)
if (bestScore < avgScore[0]):
list_of_players[0].saveModel(path=pathModel)
bestScore = avgScore[0]
df = {}
arr = np.array(list_avgScore)
for i in range(arr.shape[1]):
df[list_of_players[i].name] = arr[:, i]
df = pd.DataFrame(df)
df.to_csv(os.path.join(pathData, list_of_players[0].name + '.csv'))
