hermes-atropos-environments

To build reinforcement-learning environments for Hermes Atropos, you extend the HermesAgentBaseEnv interface, define custom reward structures, and wire the environment into the Atropos framework for training workflows.

The Atropos framework supports three CLI modes for evaluating RL environments: serve, process, and evaluate, which enable end-to-end assessments and multi-turn agent loops with tool usage.

Wandb logging integrates with Hermes agent environments to track evaluation metrics during reinforcement-learning training. It is wired into the environment setup to provide end-to-end assessment data for the agent loops.

Yes, you can implement multi-turn agent loops with tool usage in Atropos training by following Hermes agent conventions when extending HermesAgentBaseEnv to define the environment's interaction logic.

Defining reward structures is required when creating Hermes RL environments to guide the reinforcement-learning model's behavior. These structures are implemented within the HermesAgentBaseEnv interface to shape agent training.