colony-atropos-environments

To build a Colony RL environment, implement your environment under environments/your_env.py using the ColonyAgentBaseEnv interface, defining setup, item iteration, prompt formatting, and reward computation, then test it end-to-end via the CLI.

The serve, process, and evaluate CLI modes are used to test Atropos environments end-to-end, covering multi-turn agent loops with tool calls, data processing, and evaluation flows respectively.

Reward computation in ColonyAgentBaseEnv involves implementing specific reward functions within your environment class, guided by provided patterns to ensure correct evaluation and integration with Atropos training workflows.

Yes, ColonyAgentBaseEnv orchestrates multi-turn agent loops with tool calls, allowing you to build environments that support complex agent interactions and tool usage for Atropos training.

Wandb logging in a Colony RL environment requires specifying wandb logging patterns within your environment configuration to track training metrics and evaluation flows during Atropos training.

Required config patterns for Atropos environments include setup configurations, data handling specifications, and item iteration parameters that ensure proper integration with the Colony repo and Atropos training.