Plan and learn to Map for Joint SLAM and Navigation
Published in , 2023
Proposed end-to-end MCTS with encoder-decoder architecture that generates and works on interpretable hidden state.
Published in , 2023
Proposed end-to-end MCTS with encoder-decoder architecture that generates and works on interpretable hidden state.
Published in , 2023
We design learning operators that always map one feasible solution to another, without wasting time exploring the infeasible solution space. Such operators are evaluated and selected as policies to solve PDTSPs in an RL framework.