<p><strong>Evolution Strategies</strong> optimization happens on a population level. An evolution strategy algorithm in an iterative fashion (i) samples a batch of candidate solutions from the search space (ii) evaluates them and (iii) discards the ones with low fitness values. The sampling for a new iteration (or generation) happens around the mean of the best scoring candidate solutions from the previous iteration. Doing so enables evolution strategies to direct the search towards a promising location in the search space.</p>
<p><strong>Reinforcement learning</strong> requires the problem to be formulated as a Markov Decision Process (MDP). An RL agent optimizes its behavior (or policy) by maximizing a cumulative reward signal received on a transition from one state to another. Since the problem is abstracted as an MDP learning can happen on a step or episode level. Learning per step (or N steps) is done via temporal-Difference learning (TD) and per episode is done via Monte Carlo methods. So far I am talking about learning via action-value functions (learning the values of actions). Another way of learning is by optimizing the parameters of a neural network representing the policy of the agent directly via gradient ascent. This approach is introduced in the REINFORCE algorithm and the general approach known as policy-based RL.</p>
<p>For a comprehensive comparison check out this paper <a href="https://arxiv.org/pdf/2110.01411.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/2110.01411.pdf</a></p>
Difference between Evolutionary Strategies and Reinforcement Learning?
<p>I am learning about the approach employed in Reinforcement Learning for robotics and I came across the concept of Evolutionary Strategies. But I couldn't understand how  RL and ES are different. Can anyone please explain?</p>