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Users can customize the core MDP components — observations, commands, actions, rewards, events, and terminations — when training RL agents using GRID. By configuring these elements, you can tailor environment feedback and control interfaces to fit specific robotics tasks and training objectives.

Observations

Observations list each measurable quantity the environment can return, the configuration options for specifying which asset or sensor to observe, and a description of what the measurement represents.

Robot-Specific Observation Terms

Commands

Commands define the functions available to sample or set asset poses or velocities, along with the configurable parameters that control how those commands are generated or applied.

Actions

Actions enumerate the terms that transform high-level input vectors into specific joint or base control targets — such as positions, velocities, or torques—along with their associated configuration options.

Rewards

Rewards describe the available functions for assigning scalar feedback to agent behaviors, detail any options required to configure them, and explain the physical or task-related criterion they encourage or penalize.

Task-Specific Reward Terms

Robot-Specific Reward Terms

Events

Events list environment-level operations that introduce randomized or deterministic changes — such as randomizing physical properties or applying external forces—along with the parameters needed to specify how those changes are applied.

Terminations

Terminations specify the conditions under which an episode ends, the configuration options that define threshold values or monitored signals, and a brief explanation of what triggers each termination.

Robot-Specific Termination Terms

Task-Specific Termination Terms