hermes-atropos-environments

To build a reinforcement learning environment for the Hermes Agent, you construct an environment adhering to the HermesAgentBaseEnv interface. This ensures compatibility with the Atropos training framework by implementing reward functions and agent loop integration.

Integrating an RL environment with the Atropos training framework requires Python and the Hermes Agent library. You must implement the agent loop, handle tool contexts, and verify reward functions to align with the HermesAgentBaseEnv interface for training.

Yes, the Hermes Agent supports wandb logging for RL environment evaluation. You can evaluate reinforcement learning environments using wandb logging and CLI modes to monitor training performance and debug reward functions within the Atropos framework.

You test and debug reward functions for Hermes Agent RL environments by running evaluation tools that utilize wandb logging and CLI modes. This verifies that your reinforcement learning environment behaves correctly within the Atropos training framework.

No, constructing and testing these specific reinforcement learning environments requires the Hermes Agent dependency. The environment structure strictly adheres to the HermesAgentBaseEnv interface to function correctly within the Atropos training framework.