Reward function specification for real-world reinforcement learning

Determine how to specify reward functions in reinforcement learning that reliably induce desired behavior across the full range of operating conditions encountered in real-world physical deployments of embodied agents.

Background

In contrasting reinforcement learning (RL) with active inference, the authors highlight that RL typically relies on designer-specified reward functions and separate modules for uncertainty handling and exploration.

They argue that crafting reward functions that produce robust, desired behavior across diverse real-world conditions is difficult and identify this as an unresolved issue, motivating the unified objective of variational free energy in active inference.

References

Specifying a reward function that produces the desired behavior across the full range of operating conditions encountered in physical deployment is notoriously difficult and remains an open problem.

— Active Inference for Physical AI Agents -- An Engineering Perspective  (2603.20927 - Vries, 21 Mar 2026) in Section 8.3, Active Inference vs. Reinforcement Learning