environments

Design reusable RL environments by implementing the Env protocol for lifecycle, observations, actions, and rewards, then wire it into an EnvGroupBuilder for structured single- and multi-turn interactions during agent training.

The Env protocol is an interface pattern that structures reinforcement learning environment lifecycles by standardizing observations, actions, and rewards to ensure reproducible agent training and evaluation.

Structure multi-turn reinforcement learning interactions by using the EnvGroupBuilder to group Env instances, enabling consistent evaluation protocols across complex, multi-step agent training workflows.

Yes, RLDataset patterns structure dataset workflows for reinforcement learning, allowing you to manage observation and reward data consistently within both prototype experimentation and production-style pipelines.

Building custom RL environments without standardized protocols is error-prone and time-consuming, making it difficult to maintain consistent interfaces for observations, actions, and rewards across different agent training experiments.

Yes, prebuilt reference environments like Math RL env, Harbor RL env, and message-based env templates are available to accelerate integration and provide examples of consistent Env interface implementation.