Struct ReplicatorDynamicsMetaSolver 

pub struct ReplicatorDynamicsMetaSolver { /* private fields */ }

Replicator-dynamics meta-solver.

Projected replicator dynamics: iterate x_i ← x_i * (1 + η * (f_i − x · f)) followed by a non-negative renormalization, where f_i = Σ_j A[i][j] x_j is the expected row payoff for pure strategy i against the current mixture, and η is a step size. For symmetric zero-sum games this converges to a symmetric Nash equilibrium (Hofbauer & Sigmund 2003) without needing an LP solver. Slightly faster than fictitious play on continuous-payoff matrices but less robust to ties.

Implementations

impl ReplicatorDynamicsMetaSolver

pub fn new(iterations: usize, step_size: f32) -> Self

Construct with iterations updates at the given step_size.

Trait Implementations

impl Clone for ReplicatorDynamicsMetaSolver

fn clone(&self) -> ReplicatorDynamicsMetaSolver

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for ReplicatorDynamicsMetaSolver

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for ReplicatorDynamicsMetaSolver

fn default() -> Self

Returns the “default value” for a type.

impl MetaSolver for ReplicatorDynamicsMetaSolver

fn solve(&self, payoffs: &[Vec<f32>]) -> Vec<f32>

Solve for the row-player mixed-Nash on a symmetric n × n empirical payoff matrix.

fn name(&self) -> &'static str

Human-readable name for diagnostics / logging.

fn solve_n_player( &self, _payoffs: &[Vec<f32>], num_agents: usize, _per_role_k: usize, ) -> Vec<f32>

N-player solve over an explicit per-agent payoff tensor.