Episodic memory lets reinforcement learning algorithms remember and exploit promising experience from the past to improve agent performance. Previous works on memory mechanisms show benefits of using episodic-based data structures for discrete action problems in terms of sample-efficiency. The application of episodic memory for continuous control with a large action space is not trivial. Our study aims to answer the question: can episodic memory be used to improve agent's performance in continuous control? Our proposed algorithm combines episodic memory with Actor-Critic architecture by modifying critic's objective. We further improve performance by introducing episodic-based replay buffer prioritization. We evaluate our algorithm on OpenAI gym domains and show greater sample-efficiency compared with the state-of-the art model-free off-policy algorithms.

GEOMETRY is a software package for the analysis of nucleotide sequences using the method of statistical geometry in sequence space. The package consists of programs performing estimation of the average geometry of sequence quartets, analysis of positional variability and computer simulation of parallel and tree-like sequence divergence with user-defined parameters. It provides an independent tool for evaluation of the reliability of conventional phylogenetic trees and calibration of the time of sequence divergence. GEOMETRY may be of interest for all scientists engaged in the study of molecular phylogeny. The package is available by anonymous FTP from ftp.bionet.nsk.su, directory /incoming/molevol/geom.exe, and will be available from EMBL file server (URL: http://@www.ebi.ac.uk)