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• Planner: Uses Monte Carlo Tree Search (MCTS) with learned dynamics priors. Node evaluations combine value networks and probabilistic constraints.
• Policy Learning: Off-policy actor-critic (e.g., Soft Actor-Critic) with auxiliary imitation loss when expert traces available.
• Meta-Update: Reptile-style gradient aggregation across tasks to produce initialization parameters that adapt in few gradient steps.
• Arbitration Mechanism: Computes uncertainty estimates from both planner (ensemble rollouts) and policy (dropout-based Bayesian approximation); picks action set maximizing expected utility under risk constraints.

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