Upside-Down Reinforcement Learning Can Diverge in Stochastic Environments With Episodic Resets. (arXiv:2205.06595v1 [stat.ML])

Upside-Down Reinforcement Learning (UDRL) is an approach for solving RL
problems that does not require value functions and uses only supervised
learning, where the targets for given inputs in a dataset do not change over
time. Ghosh et al. proved that Goal-Conditional Supervised Learning (GCSL) --
which can be viewed as a simplified version of UDRL -- optimizes a lower bound
on goal-reaching performance. This raises expectations that such algorithms may
enjoy guaranteed convergence to the optimal policy in arbitrary …

arxiv learning ml reinforcement reinforcement learning stochastic