… which optimizes its policy to minimize … evolved policy gradient algorithm (EPG) achieves
faster learning on several randomized environments compared to an off-the-shelf policy gradient …

… A reinforcement learning algorithm, named policy gradient, is applied to learn an agent (ie, …
differential evolution adaptively during the search procedure. The differential evolution …

This research reports on the recent development of black-box optimization methods based
on single-step deep reinforcement learning and their conceptual similarity to evolution …

… Policy Gradient Assisted MAP-Elites (PGA-MAP-Elites) is an extension of MAP-Elites that …
evolving DNN controllers by combining the search power and data-efficiency of Policy Gradient …

… Policy Gradient (ddpg) algorithm, a sample efficient off-policy … Deterministic policy gradient
(td3), another off-policy deep RL … mixing mechanism over the evolution of performance, for …

… using gradient descent to find the optimal agent. One of the basic policy gradient algorithms
… ES for comparison between the gradient-based method and the evolutionary algorithm. The …

… exact 2,000,000 samples be used for evolving good feature networks. Lastly, we will
consume another 7,990,000 samples to train policy network in the policy gradient search stage. …

… Policy gradient methods remain one of the few exceptions and have found a variety of
applications. Nevertheless, the application of such methods is not without peril if done in an …

… gradient of the policy parameterization. Because the expression above is zero, we can subtract
it from the policy gradient … proceed without affecting the expected evolution of fw and 1r. …

… The key idea is to model the meta-agent’s own learning process so that its updated policy
performs better than an evolving opponent. However, prior work does not directly consider the …