Multi-Batch Experience Replay For Fast Convergence Of Continuous Action Control

Multi-Batch Experience Replay For Fast Convergence Of Continuous Action Control. Introduction replay memory is an essential concept in deep reinforcement learning since it enables the algorithms to reuse the observed streams of experiences to. Web policy gradient methods for direct policy optimization are widely considered to obtain optimal policies in continuous markov decision process (mdp) environments.

Web numerical results show that the proposed method significantly increases the speed and stability of convergence on various continuous control tasks compared to original. Introduction replay memory is an essential concept in deep reinforcement learning since it enables the algorithms to reuse the observed streams of experiences to. Web policy gradient methods for direct policy optimization are widely considered to obtain optimal policies in continuous markov decision process (mdp) environments.