Monte Carlo Tree Search with Tensor Factorization for Optimization Problems in Robotics
The paper introduces Tensor Train Tree Search (TTTS) to overcome the combinatorial complexity of Monte Carlo Tree Search (MCTS) in robotics optimization problems. TTTS uses tensor factorization to represent decision trees compactly, reducing computation and storage. This allows for efficient global decision-making across diverse tasks like inverse kinematics and motion planning, achieving high performance comparable to or better than existing methods.