dc.contributor.author |
Nahirnyi, Oleksii
|
|
dc.date.accessioned |
2022-07-22T10:01:28Z |
|
dc.date.available |
2022-07-22T10:01:28Z |
|
dc.date.issued |
2022 |
|
dc.identifier.citation |
Nahirnyi, Oleksii. Reinforcement Learning Agents in Procedurally-generated Environments with Sparse Rewards / Oleksii Nahirnyi; Supervisor: Dr. Pablo Maldonado; Ukrainian Catholic University, Faculty of Applied Sciences, Department of Computer Sciences. – Lviv 2022. – 45 p. |
uk |
dc.identifier.uri |
https://er.ucu.edu.ua/handle/1/3165 |
|
dc.description.abstract |
Solving sparse-reward environments is one of the most considerable challenges for
state-of-the-art (SOTA) Reinforcement Learning (RL). Recent usage of sparse-rewards
in procedurally-generated environments (PGE) to more adequately measure agent’s
generalization capabilities via randomization makes this challenge even harder. Despite
some progress of newly created exploration-based algorithms in MiniGrid PGEs,
the task remains open for research in terms of improving sample complexity. We
contribute to solving this task by creating a new formulation of exploratory intrinsic
reward. We base this formulation on a thorough review and categorization of other
methods in this area. Agent that optimizes an RL objective with such a formulation
performs better than SOTA methods in some small or medium sized PGEs. |
uk |
dc.language.iso |
en |
uk |
dc.subject |
reinforcement learning |
uk |
dc.subject |
exploration |
uk |
dc.subject |
sparse rewards |
uk |
dc.subject |
procedurally-generated environment |
uk |
dc.subject |
intrinsic reward |
uk |
dc.title |
Reinforcement Learning Agents in Procedurally-generated Environments with Sparse Rewards |
uk |
dc.type |
Preprint |
uk |
dc.status |
Публікується вперше |
uk |