@ai-on-browser/data-analysis-models
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Data analysis model package without any dependencies
76 lines (75 loc) • 1.92 kB
TypeScript
/**
* Draughts environment
*/
export default class DraughtsRLEnvironment extends RLEnvironmentBase {
static EMPTY: number;
static RED: number;
static WHITE: number;
static KING: number;
static OWN: number;
static OTHER: number;
_size: number[];
_board: DraughtsBoard;
_reward: {
win: number;
lose: number;
step: number;
};
get actions(): number[][];
get states(): number[][];
set evaluation(func: any);
_evaluation: (board: any, turn: any) => any;
_makeState(board: any, agentturn: any, gameturn: any): any[];
_state2board(state: any, turn: any): DraughtsBoard;
_checkAgent(agent: any): void;
reset(): any[];
_agents: number[];
_turn: any;
state(agent: any): any[];
setState(state: any, agent: any): void;
step(action: any, agent: any): {
state: any[];
reward: number;
done: boolean;
invalid?: boolean;
};
test(state: any, action: any, agent: any): {
state: any;
reward: number;
done: boolean;
invalid: boolean;
} | {
state: any[];
reward: number;
done: boolean;
invalid?: undefined;
};
}
import { RLEnvironmentBase } from './base.js';
declare class DraughtsBoard {
constructor(size: any, evaluator: any);
_evaluator: any;
_size: any;
_lines: number;
get size(): any;
get count(): {
red: any;
white: any;
redking: any;
whiteking: any;
};
get finish(): boolean;
get winner(): 2 | 4;
toString(): string;
nextTurn(turn: any): 2 | 4;
copy(): DraughtsBoard;
score(turn: any): any;
_num_to_pos(n: any): any;
at(p: any): any;
set(p: any, turn: any): boolean;
reset(): void;
_board: any[];
choices(turn: any): any[];
allPath(x: any, y: any, turn: any, first?: boolean): any;
}
export {};