ExperienceReplay.py

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