if next_move_val < min_val:
min_val = next_move_val
best_move = m
if next_move_val < 0:
break
if i == 0:
return best_move
return max_val if i % 2 == 0 else min_val
if (x_first):
print("valid moves are ", game.valid_moves)
move = int(input("player one(X), make first move: "))
game.make_move(move)
while not game.is_over():
if game.player == X:
print("valid moves are ", game.valid_moves)
move = int(input("please make move: "))
else:
move = minimax(game, 0)
print("computer is now making move ", move)
game.make_move(move)
print(game)
if game.winner_exists():
print("You win!" if game.get_winner() == X else "You lose!")
else:
print("player one and two ties")
game.board.reshape(-1))]
y[0][move] = reward
loss = criterion(q, y)
losses += loss.item()
counter += 1
if counter % 1000 == 0:
avg_losses.append(str(losses / 1000))
losses = 0
optimizer.zero_grad()
loss.backward()
optimizer.step()
y_turn = False
else:
game.make_move(get_random_valid_move(game))
y_turn = True
state = t.Tensor(game.board.reshape(1, -1))
if game.is_over():
break
if epsilon > 0.05:
epsilon -= 2 / epochs
t.save(model.state_dict(), "q_learning_meta/model.pth")
f = open("q_learning_meta/losses_over_time", "w")
f.write("\n".join(avg_losses))
end = time.time()
print(end - start)