claude-flow
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Ruflo - Enterprise AI agent orchestration for Claude Code. Deploy 60+ specialized agents in coordinated swarms with self-learning, fault-tolerant consensus, vector memory, and MCP integration
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TypeScript
/**
* Decision Transformer
*
* Implements sequence modeling approach for RL:
* - Trajectory as sequence: (s, a, R, s, a, R, ...)
* - Return-conditioned generation
* - Causal transformer attention
* - Offline RL from trajectories
*
* Performance Target: <10ms per forward pass
*/
import type { DecisionTransformerConfig, Trajectory } from '../types.js';
/**
* Default Decision Transformer configuration
*/
export declare const DEFAULT_DT_CONFIG: DecisionTransformerConfig;
/**
* Sequence entry for transformer
*/
interface SequenceEntry {
returnToGo: number;
state: Float32Array;
action: number;
timestep: number;
}
/**
* Decision Transformer Implementation
*/
export declare class DecisionTransformer {
private config;
private stateEmbed;
private actionEmbed;
private returnEmbed;
private posEmbed;
private attentionWeights;
private ffnWeights;
private actionHead;
private trajectoryBuffer;
private stateDim;
private numActions;
private updateCount;
private avgLoss;
constructor(config?: Partial<DecisionTransformerConfig>);
/**
* Add trajectory for training
*/
addTrajectory(trajectory: Trajectory): void;
/**
* Train on buffered trajectories
* Target: <10ms per batch
*/
train(): {
loss: number;
accuracy: number;
};
/**
* Get action conditioned on target return
*/
getAction(states: Float32Array[], actions: number[], targetReturn: number): number;
/**
* Forward pass through transformer
*/
forward(sequence: SequenceEntry[]): Float32Array;
/**
* Get statistics
*/
getStats(): Record<string, number>;
private initEmbedding;
private initWeight;
private createSequence;
private transformerLayer;
private updateWeights;
private softmax;
private argmax;
private hashAction;
}
/**
* Factory function
*/
export declare function createDecisionTransformer(config?: Partial<DecisionTransformerConfig>): DecisionTransformer;
export {};
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