StructRL: Model-Free Reinforcement Learning with Replay Graphs

arXiv:2611.14599v1 Announce Type: new Abstract: StructRL turns the experience collected during reinforcement learning into an explicit replay graph. Once the graph contains a path to the task goal, dynamic programming selects actions on the observed part of the state space; these actions form a graph policy that is defined only where the required transitions have been observed. StructRL transfers these actions to model-free learners: as action preferences for a DQN Q-model and as target directions along shortest observed paths for a SAC actor. For DQN on three sparse-reward MiniGrid tasks, final success rises from 0.10 to 0.90 (DoorKey), 0.10 to 0.80 (KeyCorridor) and 0 to 0.50 (ObstructedMaze); plain DQN does not catch up with three times the budget.