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Self-Improving Agents

Build agents that learn from experience and improve over time

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Learning Strategies

Four core strategies enable self-improvement: Experience Replay (learn from stored interactions), Online Learning (adapt in real-time), Meta-Learning (learn how to learn), and Self-Reflection (critique and improve outputs). Each strategy suits different scenarios and data availability.

Interactive: Strategy Explorer

Explore different learning strategies and their applications:

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Experience Replay

Store successful/failed interactions in memory. Replay them during training to reinforce patterns.

Use Case
Customer support: Learn from 10K past conversations without repeating them
Data Required
Conversation logs with outcomes (success/failure)

Interactive: Experience Replay Demo

Watch how experience replay learns from stored interactions:

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Step 1: Collect
Store interaction: query, response, outcome (success/fail)
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Step 2: Sample
Randomly select batch from memory (balanced success/fail)
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Step 3: Replay
Feed batch to agent as training examples
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Step 4: Update
Adjust weights/prompts to improve performance
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Step 5: Repeat
Cycle continues with new interactions

Choosing the Right Strategy

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Experience Replay: Best for batch learning from historical data. Stable, efficient, no catastrophic forgetting.
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Online Learning: Best for personalization and rapid adaptation. Risk of overfitting to recent examples.
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Meta-Learning: Best for multi-task agents. Requires diverse task distribution for training.
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Self-Reflection: Best for complex outputs (code, reports). Agent must have evaluation capability.
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Hybrid Approach

Combine strategies for maximum effectiveness. Example: Use Experience Replay as foundation (stable learning), add Online Learning for personalization (fast adaptation), enable Self-Reflection for critical outputs (quality control). This balances stability, speed, and accuracy.

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