Struct Agent 

pub struct Agent { /* private fields */ }

A complete MC-AIXI agent.

The agent maintains an internal world model and a planning tree. It can be used for both live interaction with an environment and for “imaginary” simulations during planning.

Implementations

impl Agent

pub fn new(config: AgentConfig) -> Self

Creates a new Agent with the given configuration.

pub fn reset(&mut self)

Resets the agent’s interaction statistics.

pub fn get_planned_action( &mut self, prev_obs_stream: &[PerceptVal], prev_rew: Reward, prev_act: Action, ) -> Action

Primary interface for decision making.

Uses MCTS to find the action that maximizes expected future reward.

pub fn model_update_percept(&mut self, observation: PerceptVal, reward: Reward)

Updates the world model with real-world percepts.

pub fn model_update_percept_stream( &mut self, observations: &[PerceptVal], reward: Reward, )

Updates the world model with an observation stream and a terminal reward.

pub fn observation_repr_from_stream( &self, observations: &[PerceptVal], ) -> Vec<PerceptVal>

Computes the observation key used for search-tree branching.

pub fn model_update_action_external(&mut self, action: Action)

Explicitly updates the world model with an action.

Trait Implementations

impl AgentSimulator for Agent

fn get_num_actions(&self) -> usize

Returns the number of possible actions the agent can perform.

fn get_num_observation_bits(&self) -> usize

Returns the bit-width used to encode observations.

fn observation_stream_len(&self) -> usize

Returns the number of observation symbols per action.

fn observation_key_mode(&self) -> ObservationKeyMode

Returns the observation key mode for search-tree branching.

fn get_num_reward_bits(&self) -> usize

Returns the bit-width used to encode rewards.

fn horizon(&self) -> usize

Returns the planning horizon (depth of simulations).

fn max_reward(&self) -> Reward

Returns the maximum possible reward value.

fn min_reward(&self) -> Reward

Returns the minimum possible reward value.

fn reward_offset(&self) -> i64

Returns the reward offset used to ensure encoded rewards are non-negative.

fn get_explore_exploit_ratio(&self) -> f64

Returns the exploration-exploitation constant (often denoted as C).

fn discount_gamma(&self) -> f64

Returns the discount factor for future rewards.

fn model_update_action(&mut self, action: Action)

Updates the internal model state with a simulated action.

fn gen_percept_and_update(&mut self, bits: usize) -> u64

Generates a simulated percept and updates the model state.

fn gen_percepts_and_update(&mut self) -> (Vec<PerceptVal>, Reward)

Helper to generate a percept stream, update the model, and return a search key + reward.

fn gen_range(&mut self, end: usize) -> usize

Generates a random value in [0, end).

fn gen_f64(&mut self) -> f64

Generates a random f64 in [0, 1).

fn model_revert(&mut self, steps: usize)

Reverts the model state to a previous point in the simulation.

fn boxed_clone_with_seed(&self, seed: u64) -> Box<dyn AgentSimulator>

Creates a boxed clone of this simulator, re-seeding any RNG state.

fn observation_repr_from_stream( &self, observations: &[PerceptVal], ) -> Vec<PerceptVal>

Returns the observation representation used for tree branching.

fn boxed_clone(&self) -> Box<dyn AgentSimulator>

Creates a boxed clone of this simulator for parallel search.

fn norm_reward(&self, reward: f64) -> f64

Normalizes a reward value to [0, 1] based on the agent’s range and horizon.