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Is every Epsilon-soft policy also Epsilon-greedy?
This assumes that Q is a Numpy-like array with 2 dimensions corresponding to the possible states and actions. Based on this, we can see that all epsilon-greedy policies are epsilon-soft policies, but not all epsilon-soft policies are epsilon-greedy policies.
What is an epsilon-greedy policy?
Epsilon-Greedy is a simple method to balance exploration and exploitation by choosing between exploration and exploitation randomly. The epsilon-greedy, where epsilon refers to the probability of choosing to explore, exploits most of the time with a small chance of exploring.
What is Epsilon-soft?
An epsilon-soft policy is a policy that takes every action with a probability of at least epsilon in every state.
Why is Epsilon-greedy Q learning?
The epsilon-greedy approach selects the action with the highest estimated reward most of the time. The aim is to have a balance between exploration and exploitation. Exploration allows us to have some room for trying new things, sometimes contradicting what we have already learned.
Why is Epsilon greedy Q learning?
How is learning rate related to Epsilon greedy policy?
Find more details on epsilon-greedy policy here. In conclusion learning rate is associated with how big you take a leap and epsilon is associated with how random you take an action. As the learning goes on both should decayed to stabilize and exploit the learned policy which converges to an optimal one.
When to use Epsilon in a greedy method?
Epsilon is used when we are selecting specific actions base on the Q values we already have. As an example if we select pure greedy method ( epsilon = 0 ) then we are always selecting the highest q value among the all the q values for a specific state.
How is Epsilon greedy used in reinforcement learning?
And when it exploits, it might get more reward. It cannot, however, choose to do both simultaneously, which is also called the exploration-exploitation dilemma. Epsilon-Greedy is a simple method to balance exploration and exploitation by choosing between exploration and exploitation randomly.
When does learning rate decay in Epsilon greedy?
As the learning goes on both should decayed to stabilize and exploit the learned policy which converges to an optimal one. As the answer of Vishma Dias described learning rate [decay], I would like to elaborate the epsilon-greedy method that I think the question implicitly mentioned a decayed-epsilon-greedy method for exploration and exploitation.