Struct ParallelUctPlanner 

pub struct ParallelUctPlanner { /* private fields */ }

Explicit parallel UCT planner state.

This backend implements explicit WU-UCT accounting and BU-core thresholding plus grouped backpropagation over deterministic completion epochs. It does not claim the full supplementary BU-UCT expansion scheduler.

Implementations

impl ParallelUctPlanner

pub fn new( workers: NonZeroUsize, bu_uct_m_max: Option<f64>, ) -> Result<Self, ParallelUctPlannerInitError>

Construct a parallel UCT planner.

workers is type-enforced non-zero. bu_uct_m_max == None selects WU-UCT; Some(x) with x \in (0, 1) selects BU-UCT thresholding.

pub fn search( &mut self, agent: &mut dyn AgentSimulator, prev_obs_stream: &[PerceptVal], prev_rew: Reward, prev_act: Action, samples: usize, ) -> Result<Action, ParallelUctSearchError>

Run a parallel UCT search and return the recommended next action.

This is the validated public entry point. It enforces the planner’s search-time contract before delegating to the internal driver shared with the in-crate, already-validated MC-AIXI call sites.

Parameters

• agent: simulator providing the action alphabet, planning horizon, reward bounds, and rollout state. Mutated through the simulator’s own contract during expansion and rollouts.

• prev_obs_stream: most recent observation, decomposed into its per-bit PerceptVal symbols, used to root the search tree.

• prev_rew: reward delivered alongside prev_obs_stream.

• prev_act: action that produced (prev_obs_stream, prev_rew).

• samples: simulation budget (number of rollouts). 0 is a valid request: it prunes the retained root to the supplied percept history and then selects an action from the currently retained completed root statistics without launching any rollouts.

  If no completed root action values are available, the returned action is chosen uniformly at random from the action alphabet.

Errors

Returns ParallelUctSearchError::PositiveSamplesRequirePositiveHorizon when samples > 0 and agent.horizon() == 0. No worker tasks are spawned and no planner search state is mutated when the contract fails.

Concurrency and cost

Dispatch parallelism is capped by workers (fixed at construction; see ParallelUctPlanner::new) and by the requested samples (small budgets may use fewer worker tasks). Completed rollout batches are evaluated through Rayon. A successful positive-budget call performs exactly samples rollout dispatches, each bounded by agent.horizon(), and applies their updates in deterministic completion-epoch order.

Randomness

search consumes randomness from agent to seed per-task simulator clones and to break ties between equal-valued actions (and, when no completed root statistics exist, to fall back to a uniform random action). As a result, even a samples == 0 call may advance the simulator RNG state.