备战世界杯！先用深度学习与强化学习踢场 FIFA 18

class ExperienceReplay(object):
    """
    During gameplay all the experiences < s, a, r, s’ > are stored in a replay memory.
    In training, batches of randomly drawn experiences are used to generate the input and target for training.
    """

    def __init__(self, max_memory=100000, discount=.9):
        """
        Setup
        max_memory: the maximum number of experiences we want to store
        memory: a list of experiences
        discount: the discount factor for future experience

        In the memory the information whether the game ended at the state is stored seperately in a nested array
        [...
        [experience, game_over]
        [experience, game_over]
        ...]
        """
        self.max_memory = max_memory
        self.memory = list()
        self.discount = discount

    def remember(self, states, game_over):
        # Save a state to memory
        self.memory.append([states, game_over])
        # We don't want to store infinite memories, so if we have too many, we just delete the oldest one
        if len(self.memory) > self.max_memory:
            del self.memory[0]

    def get_batch(self, model, batch_size=10):

        # How many experiences do we have?
        len_memory = len(self.memory)

        # Calculate the number of actions that can possibly be taken in the game.
        num_actions = model.output_shape[-1]

        # Dimensions of our observed states, ie, the input to our model.
        env_dim = self.memory[0][0][0].shape[1]

        # We want to return an input and target vector with inputs from an observed state.
        inputs = np.zeros((min(len_memory, batch_size), env_dim))

        # ...and the target r + gamma * max Q(s’,a’)
        # Note that our target is a matrix, with possible fields not only for the action taken but also
        # for the other possible actions. The actions not take the same value as the prediction to not affect them
        targets = np.zeros((inputs.shape[0], num_actions))

        # We draw states to learn from randomly
        for i, idx in enumerate(np.random.randint(0, len_memory,
                                                  size=inputs.shape[0])):
            """
            Here we load one transition <s, a, r, s’> from memory
            state_t: initial state s
            action_t: action taken a
            reward_t: reward earned r
            state_tp1: the state that followed s’
            """
            state_t, action_t, reward_t, state_tp1 = self.memory[idx][0]

            # We also need to know whether the game ended at this state
            game_over = self.memory[idx][1]

            # add the state s to the input
            inputs[i:i + 1] = state_t

            # First we fill the target values with the predictions of the model.
            # They will not be affected by training (since the training loss for them is 0)
            targets[i] = model.predict(state_t)[0]

            """
            If the game ended, the expected reward Q(s,a) should be the final reward r.
            Otherwise the target value is r + gamma * max Q(s’,a’)
            """
            #  Here Q_sa is max_a'Q(s', a')
            Q_sa = np.max(model.predict(state_tp1)[0])

            # if the game ended, the reward is the final reward
            if game_over:  # if game_over is True
                targets[i, action_t] = reward_t
            else:
                # r + gamma * max Q(s’,a’)
                targets[i, action_t] = reward_t + self.discount * Q_sa
        return inputs, targets

# parameters
max_memory = 1000  # Maximum number of experiences we are storing
batch_size = 1  # Number of experiences we use for training per batch

exp_replay = ExperienceReplay(max_memory=max_memory)


# Train a model on the given game
def train(game, model, epochs, verbose=1):
    num_actions = len(game.key_to_press)  # 4 actions [shoot_low, shoot_high, left_arrow, right_arrow]
    # Reseting the win counter
    win_cnt = 0
    # We want to keep track of the progress of the AI over time, so we save its win count history 
    # indicated by number of goals scored
    win_hist = []
    # Epochs is the number of games we play
    for e in range(epochs):
        loss = 0.
        # epsilon for exploration - dependent inversely on the training epoch
        epsilon = 4 / ((e + 1) ** (1 / 2))
        game_over = False
        # get current state s by observing our game environment
        input_t = game.observe()

        while not game_over:
            # The learner is acting on the last observed game screen
            # input_t is a vector containing representing the game screen
            input_tm1 = input_t

            # We choose our action from either exploration (random) or exploitation (model).
            if np.random.rand() <= epsilon:
                # Explore a random action
                action = int(np.random.randint(0, num_actions, size=1))
            else:
                # Choose action from the model's prediction
                # q contains the expected rewards for the actions
                q = model.predict(input_tm1)
                # We pick the action with the highest expected reward
                action = np.argmax(q[0])

            # apply action, get rewards r and new state s'
            input_t, reward, game_over = game.act(action)
            # If we managed to score a goal we add 1 to our win counter
            if reward == 1:
                win_cnt += 1

            """
            The experiences < s, a, r, s’ > we make during gameplay are our training data.
            Here we first save the last experience, and then load a batch of experiences to train our model
            """
            # store experience
            exp_replay.remember([input_tm1, action, reward, input_t], game_over)

            # Load batch of experiences
            inputs, targets = exp_replay.get_batch(model, batch_size=batch_size)

            # train model on experiences
            batch_loss = model.train_on_batch(inputs, targets)

            loss += batch_loss

        if verbose > 0:
            print("Epoch {:03d}/{:03d} | Loss {:.4f} | Win count {}".format(e, epochs, loss, win_cnt))

        # Track win history to later check if our model is improving at the game over time.
        win_hist.append(win_cnt)
    return win_hist