Add reward history tracking and visualization for swarm overwatch

[binary-husky]

• Add RewardHistoryEntry and RewardHistoryResponse models for reward data
• Implement reward collection and history finalization in swarm_server
• Add /get_reward_history API endpoint for visualization
• Add ASCII reward curve display in swarm_overwatch UI
• Fix typo in config_utils parameter name (convertion_json_fg -> convertion_json_fp)

[gemini-code-assist]

Summary of Changes

Hello, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request significantly enhances the swarm_overwatch system by integrating comprehensive reward tracking and visualization capabilities. It introduces new data models for storing reward history, implements server-side mechanisms to aggregate and store reward statistics per global step, and provides a new API endpoint to access this data. Furthermore, the swarm_overwatch user interface is updated with an ASCII-based reward curve display, enabling real-time monitoring of training performance directly within the terminal. A minor but important typo in a configuration utility function has also been rectified.

Highlights

• Reward History Models: Introduced RewardHistoryEntry and RewardHistoryResponse Pydantic models for structured storage and retrieval of reward data, including global step, mean reward, standard deviation, and timestamp.
• Server-Side Reward Tracking: Implemented logic within swarm_server.py to collect individual episode rewards, aggregate them per global step, calculate mean and standard deviation, and store them as a historical record. This includes initialization of shared memory for reward tracking and a function to finalize step-wise reward statistics.
• Reward History API Endpoint: Exposed a new /get_reward_history API endpoint in swarm_server.py to allow clients to fetch the accumulated reward history for visualization purposes.
• ASCII Reward Curve Visualization: Developed an ASCII-based reward curve display in the swarm_overwatch UI, enabling users to visualize the mean reward over global steps directly in the terminal, complete with statistics and axis labels.
• Typo Correction: Fixed a typo in the align_parameters function within ajet/utils/config_utils.py, renaming the parameter convertion_json_fg to convertion_json_fp for consistency.

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[gemini-code-assist]

Code Review

The pull request successfully introduces reward history tracking and visualization for the swarm overwatch system. New Pydantic models (RewardHistoryEntry, RewardHistoryResponse) are added to define the structure of reward data. The swarm_server is updated to collect and finalize reward statistics at each global step, storing them in shared memory. A new API endpoint /get_reward_history is implemented to expose this data. The swarm_overwatch UI now includes a display_reward_curve function to visualize this history as an ASCII chart, enhancing monitoring capabilities. Additionally, a minor but important typo in config_utils.py has been corrected. The changes are well-structured and contribute positively to the system's observability.

[gemini-code-assist]

The import numpy as np statement is placed inside the _finalize_reward_history_for_step function. It is generally considered a best practice in Python to place all imports at the top of the file, outside of any functions or methods. This improves readability, makes dependencies clear at a glance, and avoids potential performance overhead from repeated imports if the function is called frequently. Please move this import to the top of the file with other imports.