> ML_ALGORITHM // MONTE-CARLO-TREE-SEARCH-ALPHAZERO_v1.0
Monte Carlo Tree Search with Neural Guidance (AlphaZero / MCTS)
Planning and reinforcement learning framework combining Monte Carlo tree search with deep neural networks for policy prior and value evaluation.
Model-Based Planning RLreinforcement-learningmoderate-posthocmassive (>10M)
Back to All AlgorithmsComputational Complexity
Training Complexity:O(simulations * depth * NN_eval)
Inference Complexity:O(MCTS_simulations * NN_forward)
Hardware Profile
CPU Friendly:No
Requires GPU:Yes
Memory Footprint:high
Interpretability & Data
Interpretability Tier:moderate-posthoc
Training Data Needs:massive (>10M)
Interpretability Assessment
Search tree can be explicitly dumped and inspected to examine candidate branches, visit counts, and expected values.
Suitable Tasks & Supported Modalities
Suitable Tasks:
reinforcement learningcombinatorial game planningreasoning search
Supported Modalities:
tabulargraph
Implementing Libraries
Foundational Literature
Mastering the game of Go with deep neural networks and tree search (AlphaGo)David Silver, Aja Huang (2016) · Nature
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm (AlphaZero)David Silver, Thomas Hubert (2017) · Science
Common Pitfalls & Warnings
- High inference latency: evaluating hundreds of MCTS rollouts per decision makes real-time low-latency serving prohibitive