RL group
The reinforcement learning group goals are to produce new theoretical results and apply them in practice. The group is focused on the development of new RL algorithms and on achieving state-of-the-art results in popular test environments.

Current projects include the task of improving the quality of solving important practical problems, for example, navigating robots in complex environments, the task of UI actions automation, the design of methods to quickly solve combinatorial optimization problems, the task of modeling the behavior of economic agents.
Focus areas
There are several known problems that hinder the effective use of RL. Those are, the issue of effective exploration of environments with a rare reward, the complications in adapting the agents to changes in the environment and catastrophic forgetting when moving to a new environment.

There are multiple ways that partially solve these problems, but there is no singular approach that flexibly combine them into a general solution that would allow to effectively apply reinforcement learning to the previously mentioned complex problems.

As part of Synergy RL approach, we propose a new reinforcement learning method that would combine individual ideas into a universal framework.
Synergy RL
We are open for cooperation. Send us an e-mail, if you would like/want to work with us/to join us.
Team lead
Dmitrii Babaev
© 2021
Russia, Moscow
Nizhny Susalny lane 5 p. 19