How do inverse reinforcement learning and forward reinforcement learning differ?
Instruction: Compare and contrast inverse reinforcement learning with the traditional, forward approach to reinforcement learning.
Updated
Example Answer
In forward reinforcement learning, a reward objective is specified and the agent learns a policy that achieves a high return. In inverse reinforcement learning, I observe behavior and infer a reward function that could explain it, usually under assumptions about the environment and the demonstrator.
The inferred reward is not necessarily the demonstrator's unique true intention. Many rewards can explain the same policy. With an all-zero reward, for example, every policy can be optimal. Useful inverse methods need additional structure, regularization, comparisons, or assumptions to narrow that ambiguity.
I would also separate inverse reinforcement learning from behavioral cloning. Cloning directly predicts demonstrated actions from observations; inverse learning estimates an objective and may then use a forward-learning or planning step. Recovering an objective can help with transfer to new situations, but errors in the dynamics model or imperfect demonstrations can make the inferred reward misleading. I would test the resulting behavior on held-out scenarios rather than judging only how well it matches the training demonstrations.
Follow-up
What extra evidence would distinguish two reward functions that explain the demonstrations equally well?