nn.ReLU(), nn.Linear(lay2, outp), nn.Sigmoid())
#player2
model2 = nn.Sequential(nn.Linear(ins, lay1), nn.ReLU(), nn.Linear(lay1, lay2),
nn.ReLU(), nn.Linear(lay2, outp), nn.Sigmoid())
#model.load_state_dict(basemodel)
loss_fn = nn.MSELoss()
op = optim.Adam(model.parameters(), lr=.001)
loss_fn2 = nn.MSELoss()
op2 = optim.Adam(model2.parameters(), lr=.001)
epochs = 5000
eps = 1
verbose = False
game = TicTacToe()
print(game.av_moves())
flag2cpu = True
gamma = .8
losses = []
losses2 = []
framest = []
tw = 0
tt = 0
tl = 0
tnm = 0
for i in range(2 * epochs):
if epochs == i:
flag2cpu = False
#epsilon decaying factor
eps = .5 + .5 * np.tanh(-10 * (i / epochs) + 2)
#print(eps)
