Files

jgrusewski 2c1acda2f3 feat: DQN Rainbow enhancements with hyperopt results and test coverage

- Update DQN trainer with gradient collapse detection warmup
- Add portfolio tracker improvements
- Include hyperopt trial results (multiple Sharpe ratio experiments)
- Add new test files for action/position sign convention, early stopping,
  cash reserve bugs, and portfolio execution
- Update trained model files
- Add Claude Code configuration and skills

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

2025-11-27 14:45:25 +01:00

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Agent Specialization Training

Purpose

Train agents to become experts in specific domains for better performance.

Specialization Areas

1. By File Type

Agents automatically specialize based on file extensions:

.js/.ts: Modern JavaScript patterns
.py: Pythonic idioms
.go: Go best practices
.rs: Rust safety patterns

2. By Task Type

Tool: mcp__claude-flow__agent_spawn
Parameters: {
  "type": "coder",
  "capabilities": ["react", "typescript", "testing"],
  "name": "React Specialist"
}

3. Training Process

The system trains through:

Successful edit operations
Code review patterns
Error fix approaches
Performance optimizations

4. Specialization Benefits

# Check agent specializations
Tool: mcp__claude-flow__agent_list
Parameters: {"swarmId": "current"}

Result shows expertise levels:
{
  "agents": [
    {
      "id": "coder-123",
      "specializations": {
        "javascript": 0.95,
        "react": 0.88,
        "testing": 0.82
      }
    }
  ]
}

Continuous Improvement

Agents share learnings across sessions for cumulative expertise!

CLI Usage

# Train agent specialization via CLI
npx claude-flow train agent --type coder --capabilities "react,typescript"

# Check specializations
npx claude-flow agent list --specializations

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