d803bfe2b1
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
40 lines
1.5 KiB
TypeScript
40 lines
1.5 KiB
TypeScript
/**
|
|
* Reinforcement Learning Training Data Generation
|
|
*
|
|
* This example demonstrates generating synthetic RL training data including:
|
|
* - State-action-reward tuples
|
|
* - Episode generation with temporal consistency
|
|
* - Exploration vs exploitation scenarios
|
|
* - Reward function testing
|
|
*/
|
|
import type { GenerationResult } from '../../src/types.js';
|
|
/**
|
|
* Generate basic SAR tuples for Q-learning
|
|
*/
|
|
export declare function generateSARTuples(): Promise<GenerationResult<unknown>>;
|
|
/**
|
|
* Generate complete RL episodes with consistent state transitions
|
|
*/
|
|
export declare function generateEpisodes(): Promise<GenerationResult<unknown>>;
|
|
/**
|
|
* Generate data for testing exploration-exploitation trade-offs
|
|
*/
|
|
export declare function generateExplorationData(): Promise<GenerationResult<unknown>>;
|
|
/**
|
|
* Generate data for testing and debugging reward functions
|
|
*/
|
|
export declare function generateRewardTestingData(): Promise<GenerationResult<unknown>>;
|
|
/**
|
|
* Generate training data for policy gradient methods
|
|
*/
|
|
export declare function generatePolicyGradientData(): Promise<GenerationResult<unknown>>;
|
|
/**
|
|
* Generate data for multi-agent reinforcement learning
|
|
*/
|
|
export declare function generateMultiAgentData(): Promise<GenerationResult<unknown>>;
|
|
/**
|
|
* Example of using generated data in a training loop
|
|
*/
|
|
export declare function trainingLoopIntegration(): Promise<void>;
|
|
export declare function runAllRLExamples(): Promise<void>;
|
|
//# sourceMappingURL=reinforcement-learning.d.ts.map
|