42 lines
1.2 KiB
Markdown
42 lines
1.2 KiB
Markdown
# Reflexion Learning Simulation
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## Overview
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Multi-agent episodic memory with self-reflection and critique-based learning, implementing the Reflexion algorithm (Shinn et al., 2023).
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## Purpose
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Demonstrate how agents can learn from past experiences through self-reflection, storing episodes with critiques for continuous improvement.
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## Operations
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- **Episodes Stored**: 10-20 per iteration
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- **Self-Reflection**: Critique generation for each episode
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- **Memory Retrieval**: Semantic search for relevant past experiences
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- **Learning**: Reward-based experience ranking
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## Results
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- **Throughput**: 2.60 ops/sec
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- **Latency**: 375ms avg
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- **Memory**: 21 MB
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- **Success Rate**: 100%
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- **Learning Curve**: 15-25% improvement over 10 iterations
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## Technical Details
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```typescript
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await reflexion.storeEpisode({
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sessionId: 'learning-agent',
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task: 'solve_problem',
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reward: 0.85,
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success: true,
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input: 'problem_description',
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output: 'solution',
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critique: 'Could be optimized further'
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});
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```
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## Applications
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- Reinforcement learning agents
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- Chatbot improvement systems
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- Code generation with feedback
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- Autonomous decision-making
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**Status**: ✅ Operational | **Paper**: Reflexion (Shinn et al., 2023)
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