hermes-atropos-environments

To create reinforcement learning environments for the Hermes Agent using Atropos, run the 'build' command. This generates a new environment that follows the HermesAgentBaseEnv interface and integrates directly with the Atropos training framework.

Integrating reward functions and orchestrating the agent loop in Atropos is handled automatically during environment creation. The generated setup ensures efficient agent loop integration and properly structures the reward functions for Hermes Agent training.

The HermesAgentBaseEnv interface is the required structural standard for RL environments built for the Hermes Agent. It ensures compatibility with the Atropos training framework by standardizing environment setup, reward integration, and agent loop orchestration.

Evaluating and logging metrics for reinforcement learning environments is supported through built-in evaluation tools. The system integrates with wandb to evaluate environments and automatically log metrics for detailed performance analysis.

Yes, you can serve, process, and evaluate RL environments using three dedicated CLI modes. These modes cater to different testing and deployment needs, allowing you to manage the Hermes Agent environment lifecycle directly from the command line.

Building RL environments with Atropos requires Python for scripting and the Atropos framework for training. You need these dependencies to handle environment setup, reward function integration, and agent loop orchestration properly.