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npm/packages/agentic-synth/examples/self-learning/reinforcement-learning.d.ts

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+/**
+ * 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