Temporal Difference Learning for Model Predictive Control · HackerPapers discussionshttps://hackerpaper.kaipfister.de/p/2203.04955/Collected HN comments and submissions linking this paper.enMon, 02 Nov 2026 11:16:22 GMTTemporal Difference Learning for Model Predictive Control — mentioned by imtringuedhttps://news.ycombinator.com/item?id=45887248urn:hackerpapers:mention:2203.04955:45887248Tue, 11 Nov 2025 13:50:40 GMT<p>What I personally find amusing is this part: &gt;3. Interactive: World models can output the next states based on input actions &gt;Finally, if actions and/or goals are part of the prompt to a world model, its outputs must include the next state of the world, represented either implicitly or explicitly. When given only an action with or without a goal state as the input, the world model should produce an output consistent with the world’s previous state, the intended goal state if any, and its semantic meanings, physical laws, and dynamical behaviors. As spatially intelligent world models become…</p><p><a href="https://hackerpaper.kaipfister.de/p/2203.04955/">Paper and discussion history</a>. A mention is not an endorsement.</p>cs.LG