05_CartPole-reinforcement-learning_PER_D3QN

SumTree_Memory.py

class Memory(object):  # stored as ( state, action, reward, next_state ) in SumTree
    PER_e = 0.01  # Hyperparameter that we use to avoid some experiences to have 0 probability of being taken
    PER_a = 0.6  # Hyperparameter that we use to make a tradeoff between taking only exp with high priority and sampling randomly
    PER_b = 0.4  # importance-sampling, from initial value increasing to 1
    
    PER_b_increment_per_sampling = 0.001
    
    absolute_error_upper = 1.  # clipped abs error

    def __init__(self, capacity):
        # Making the tree 
        self.tree = SumTree(capacity)