AI Agent Guide
Knowledge Guides /How do you design reward functions for simulation-based reinforcement learning and prevent reward gaming?
Last updated: · New Horizon Digital Outsourcing Team
Rewards should support task completion and constraint satisfaction, rather than attractive training curves. Separate completion conditions, failure conditions, and auxiliary rewards, and use evaluation metrics independent of the reward to determine whether the policy has actually learned the task.
Define observable success first
- Express task completion as conditions the environment can assess, and list termination causes such as collisions, boundary violations, and timeouts. Success criteria must not depend on hidden information available only during training; also define what can be observed at deployment. Do not begin large-scale training while task objectives remain ambiguous.
Introduce auxiliary rewards carefully
- Rewards for reducing distance or improving posture can aid learning but may encourage limited local behaviors. Check whether repeatedly approaching and retreating, delaying termination, or exploiting simulation defects can earn high scores. Document the intent and scale of every reward term.
Separate training from evaluation
- Evaluate using scenarios, seeds, and disturbance ranges not used for tuning, and report task success rates and constraint violations. If rewards increase while success rates remain unchanged, check for objective misalignment. Do not repeatedly inspect the evaluation set, continue tuning, and then call the same set independent acceptance evidence.
Deliver interpretable evidence of policy behavior
- Retain reward definitions, environment versions, training configurations, and model checkpoints, together with failure trajectories and evaluation script instructions. Passing in simulation does not mean reinforcement learning can directly control physical equipment; controlled validation and safety limits are still required before transfer.