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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 > exploration_rate:
            action = np.argmax(q_table[state,:])
        else:
            action = env.action_space.sample()
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1 Answer 1

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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)
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  • $\begingroup$ why is exploration_decay_rate negative in np.exp()? $\endgroup$
    – mogoja
    Commented Jul 29, 2020 at 17:10
  • $\begingroup$ 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. $\endgroup$
    – Rithik Banerjee
    Commented Jul 29, 2020 at 17:14

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