Papers › Backward Learning for Goal-Conditioned Policies

Backward Learning for Goal-Conditioned Policies

8 Dec 2023arXiv:2312.05044archive 2025-07-28

Marc Höftmann, Jan Robine, Stefan Harmeling

Can we learn policies in reinforcement learning without rewards? Can we learn a policy just by trying to reach a goal state? We answer these questions positively by proposing a multi-step procedure that first learns a world model that goes backward in time, secondly generates goal-reaching backward trajectories, thirdly improves those sequences using shortest path finding algorithms, and finally trains a neural network policy by imitation learning. We evaluate our method on a deterministic maze environment where the observations are 64×64 pixel bird's eye images and can show that it consistently reaches several goals.

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