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genpark-monte-carlo-tree-search-agent-planner-skill

mcp
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SUMMARY

GenPark AI Agent Skill - Monte Carlo Tree Search (MCTS) reasoning tree planner with UCB1 node selection, rollout simulation, and backpropagation.

README.md

GenPark AI Agent Skill - Monte Carlo Tree Search (MCTS) Planner

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Protocol
License

Monte Carlo Tree Search (MCTS) with UCB1 exploration-exploitation balancing for agent decision planning inspired by LATS and AlphaZero.

flowchart TD
    A[Root Problem State] --> B[Selection UCB1]
    B --> C[Action Expansion]
    C --> D[Rollout Simulation]
    D --> E[Backpropagate Value Q]
    E --> F[Optimal Strategy Selection]

Features

  • UCB1 Balance: Balances exploring uncertain branches against exploiting high-reward steps.
  • Backpropagation: Accurately aggregates downstream success signals back up to the decision root.
  • Zero Dependencies: Pure Python standard library.

Quickstart

from client import AgentMCTSPlannerClient

planner = AgentMCTSPlannerClient()
best = planner.run_mcts(state, actions_fn, eval_fn)

Ecosystem & Citations

Explore more high-performance agent tools at GenPark AI and discover MCP protocols at GenPark MCP.

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