AlphaZero-Style Search: An enhancement of MCTS that combines deep neural networks with tree search. This approach, popularized by AlphaZero, uses a policy network to guide the search and a value network to evaluate positions, which can outperform standard MCTS by focusing the search on more promising branches.
- Best-First Search (A):* Best-First Search algorithms, such as A*, prioritize expanding the most promising nodes first based on a heuristic. This can be more efficient than MCTS in environments where good heuristics are available, leading to faster convergence to optimal solutions.
- Neural Path-Consistent Search (NPCS): NPCS integrates a neural network to ensure path consistency, effectively guiding the search process to reduce the exploration of suboptimal branches. This approach can outperform MCTS in environments where consistency across decision paths is critical.
- Depth-First Iterative Deepening (DFID): DFID combines the space efficiency of depth-first search with the completeness of breadth-first search. It can be more effective than MCTS in environments where memory is constrained or where deeper searches are needed without exhaustive memory use.
- Beam Search with Pruning: Beam Search focuses on a set number of best candidates at each level, pruning less promising paths. When enhanced with heuristics or neural guidance, it can outperform MCTS by concentrating computational resources on the most likely successful paths.
- Rollout-Guided Search: Similar to MCTS, but with more sophisticated rollout policies guided by deep learning models or heuristics, this can improve the quality of the search by generating more informed simulations.
- Policy-Value Monte Carlo Tree Search (PV-MCTS): This is a variant of MCTS that integrates policy and value networks more effectively, often used in game AI. It can outperform standard MCTS in environments where the agent can be trained to evaluate states effectively.
- Lazy Tree Search (LTS): LTS delays the full expansion of nodes until necessary, saving computation by avoiding unnecessary exploration. It can be more efficient than MCTS in environments with a large branching factor.
- Graph Neural Network-Guided Search: Use of graph neural networks (GNNs) to guide the search process. This can be particularly powerful in environments where the state space can be represented as a graph, providing better exploration strategies than MCTS.
- Probabilistic Planning Algorithms (e.g., POMCP): In environments with uncertainty or partial observability, algorithms like Partially Observable Monte Carlo Planning (POMCP) can be more effective than standard MCTS by better handling probabilistic outcomes and hidden information.
1. Gradient Rewards (그라데이션 보상)
- 단순히 마지막 상태에서 보상 1을 주는 대신, 상태가 최종 목표에 얼마나 가까운지를 평가하여 보상을 부여합니다. 예를 들어, 목표에 가까워질수록 더 높은 보상을 주고, 멀어질수록 낮은 보상을 주는 방식입니다.