I wish the author had picked a title like "Single-task reinforcement learning is a waste" or "Reward function engineering is a waste". At the very end of his e

I wish the author had picked a title like "Single-task reinforcement learning is a waste" or "Reward function engineering is a waste". At the very end of his essay he mentions some directions: homeostasis, Friston's free-energy principle, and predictive processing. I agree that all of these are very interesting. A few steps less ambitious is recent work on reinforcement learning to reach desired outcomes without specifying reward functions. All of these seem to require more focus on learning the model of dynamics in the relevant domain (what happens when I take action A from state S?) and less on value / policy learning. Ok great, but why can't that happen in the context of "deep reinforcement learning"? For complex environments with partial observability, don't we probably want something like states represented in an embedding? As we improve our model, is there anything broken about the approach of optimizing a differentiable function? "There are likely better approaches to deep RL, and they include ..." seems like a better framing than "deep RL is a waste of time."