Discovering Minimal Reinforcement Learning Environments

arXiv:2406.12589v1 Announce Type: new
Abstract: Reinforcement learning (RL) agents are commonly trained and evaluated in the same environment. In contrast, humans often train in a specialized environment before being evaluated, such as studying a book before taking an exam. The potential of such specialized training environments is still vastly underexplored, despite their capacity to dramatically speed up training.
The framework of synthetic environments takes a first step in this direction by meta-learning neural network-based Markov decision processes (MDPs). The initial …

abstract agents arxiv book capacity contrast cs.lg environment environments exam humans potential reinforcement reinforcement learning studying train training type