How can I fetch exploration decay rate of an iterable Q-table in Python?

Question

I have done creating the virtual environment, creating the Q-table, initializing the q-parameters, then I made a training module and stored it in a numpy array. After completion of training, I have updated the q-table and now I get the plots for the explorations But how can I code for rate decay? Here is my sample code for every step of the training module,

for step in range(max_steps): 
        exploration_rate_threshold = random.uniform(0,1)
    if exploration_rate_threshold &gt; exploration_rate:
        action = np.argmax(q_table[state,:])
    else:
        action = env.action_space.sample()

score 1 · Accepted Answer · answered Jul 29 '20 at 16:47

1

Here is one way to calculate the exploration rate decay:

exploration_rate = min_exploration_rate + \ (max_exploration_rate - min_exploration_rate) * np.exp(-exploration_decay_rate*episode)

answered Jul 29 '20 at 16:47

Rithik Banerjee

161
1
5

why is exploration_decay_rate negative in np.exp()? – mogoja Jul 29 '20 at 17:10
on finish of every step of training, exploration rate decreases or decays at a rate proportional to its ongoing decay value. Hence, its negative for every episode. – Rithik Banerjee Jul 29 '20 at 17:14

How can I fetch ​exploration decay rate of an iterable Q-table in Python?

1 Answers1

How can I fetch exploration decay rate of an iterable Q-table in Python?