Phase 54.4 Spec & Technical Discussion: ImaginationEngine

[web3guru888]

ImaginationEngine — Spec & Technical Discussion

Technical discussion thread for Phase 54.4: ImaginationEngine (#1037).

Core Design Questions

1. Gradient-based vs search-based planning: Dreamer trains a policy via backprop through imagined trajectories; MuZero uses MCTS. Should we support both? Which is the default for different problem types?

2. Imagination budget: How many rollouts / how deep should imagination go? Adaptive computation based on decision difficulty vs. fixed budget?

3. Counterfactual generation: How do we generate meaningful counterfactuals? Random action perturbation, gradient-guided, or causal intervention (connecting to Phase 40)?

4. Parallel imagination: How do we efficiently batch thousands of imagined trajectories on GPU? Memory management for branching tree structures?

5. Dream quality vs speed: Trade-off between imagination fidelity (more detailed predictions) and speed (more trajectories explored). How do we balance?

Proposed API Surface

class ImaginationEngine:
    def imagine(self, state: LatentState, horizon: int) -> List[Trajectory]
    def plan_mcts(self, state: LatentState, budget: int) -> Action
    def plan_dreamer(self, state: LatentState) -> Action
    def counterfactual(self, state: LatentState, alt_actions: List[Action]) -> List[Trajectory]
    def get_best_plan(self) -> ActionSequence

Share your thoughts below.

Related: #1037 | #1033