Research map / Learning theory and algorithms / Combinatorial and search optimization
Monte Carlo Tree Search in Continuous Spaces Using Voronoi Optimistic Optimization with Regret Bounds
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use VOO action optimizer (VOOT) instead of RAND-DOOT / PW-UCT in MCTS | continuous MCTS · Object-clearing | simulations to clear obstacles: 1700 -> 750 | Helped |
| use VOOT instead of other continuous MCTS methods | continuous MCTS · Packing (conveyor belt) | objects placed: 12 -> 15 | Helped |
| use VOOT instead of PW-UCT | continuous MCTS · Packing (conveyor belt) | reward: below 2 -> approximately 4 | Helped |
| use VOOT tree search instead of PPO/DDPG policy search | planning · Packing (conveyor belt) | objects packed: 8 -> 13 | Helped |
| use VOO instead of DOO/SOO/GP-UCB/REMBO/BAMSOO for black-box optimization at high dimensions | budgeted black-box optimization · Griewank, Rastrigin, Shekel (up to 20-D) | best value found | Helped |
| use VOO instead of DOO in 3-dimensional functions | black-box optimization · 3-D test functions | best value found | No clear effect |
| use VOO instead of CMA-ES | black-box optimization · high-dimensional test functions | best value found | Mixed |
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