I googled “model-based RL Atari” and the first hit was this which likewise tries to learn the reward function by supervised learning from observations of past rewards (if I understand correctly)
Ah, the “model-based using a model-free RL algorithm” approach :) They learn a world model using supervised learning, and then use PPO (a model-free RL algorithm) to train a policy in it. It sounds odd but it makes sense: you hopefully get much of the sample efficiency of model-based training, while still retaining the state-of-the-art results of model-free RL. You’re right that in this setup, as the actions are being chosen by the (model-free RL) policy, you don’t get any zero-shot generalization.
I added a new sub-bullet at the top to clarify that it’s hard to explain by RL unless you assume the planner can query the ground-truth reward function in arbitrary hypothetical states. And then I also added a new paragraph to the “other possible explanations” section at the bottom saying what I said in the paragraph just above. Thank you.
Thanks for updating the post to clarify this point—I agree with you with the new wording.
In ML today, the reward function is typically a function of states and actions, not “thoughts”. In a brain, the reward can depend directly on what you’re imagining doing or planning to do, or even just what you’re thinking about. That’s my proposal here.
Yes indeed, your proposal is quite different from RL. The closest I can think of to rewards over “thoughts” in ML would be regularization terms that take into account weights or, occasionally, activations—but that’s very crude compared to what you’re proposing.