5 citations · 8 across the 4 of their papers we have counts for
5 papers
TreeDQN: Sample-Efficient Off-Policy Reinforcement Learning for Combinatorial Optimization
D. Sorokin, A. Kostin, L. Savchenko +2
A convenient approach to optimally solving combinatorial optimization tasks is the Branch-and-Bound method. Its branching heuristic can be learned to solve a large set of similar t…
Insights From the NeurIPS 2021 NetHack Challenge
Eric Hambro, Sharada Mohanty, Dmitrii Babaev +26
In this report, we summarize the takeaways from the first NeurIPS 2021 NetHack Challenge. Participants were tasked with developing a program or agent that can win (i.e., 'ascend' i…
Adaptation of Quadruped Robot Locomotion with Meta-Learning
Arsen Kuzhamuratov, Dmitry Sorokin, Alexander Ulanov +1
Animals have remarkable abilities to adapt locomotion to different terrains and tasks. However, robots trained by means of reinforcement learning are typically able to solve only a…
Aligning an optical interferometer with beam divergence control and continuous action space
Stepan Makarenko, Dmitry Sorokin, Alexander Ulanov +1
Reinforcement learning is finding its way to real-world problem application, transferring from simulated environments to physical setups. In this work, we implement vision-based al…
Interferobot: aligning an optical interferometer by a reinforcement learning agent
Dmitry Sorokin, Alexander Ulanov, Ekaterina Sazhina +1
Limitations in acquiring training data restrict potential applications of deep reinforcement learning (RL) methods to the training of real-world robots. Here we train an RL agent t…