What is the purpose of the Epsilon greedy algorithm?
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.
Which is the optimal policy for Epsilon greedy?
The theorem assumes that given policy is epsilon soft policy and shows that epsilon greedy on value function obtained by following an epsilon soft policy is optimal. The fact that policy is episolon soft ensures that weights are non negative. Note that inequality won’t hold if weights can be negative.
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.
What is π ( A | S ) in greedy policies?
Further, in determinsitic (e.g., greedy) policies, π(a | s) = 0 for a ≠ arg maxaqπ(s, a). Then the theorem tells little. Could anyone verify my understanding or shed more light in the theorem? Ok!
How to calculate distribution of arms in Epsilon greedy?
With the algorithm setup for Epsilon Greedy, we need to discuss the distribution of the arm/action. Think of each arm/action as a coin flip. The outcome of a coin flip is of a dichotomous nature, either Heads or Tails. Thus, we can implement a Bernoulli distribution for each arm.
What does Epsilon mean in the bandit algorithm?
By convention, “epsilon” represents the percentage of time/trials dedicated for exploration, and it is also typical to do random exploration. This introduces some form of stochasticity. The following analysis is based on the book “Bandit Algorithms for Website Optimization” by John Myles White.