Model-based RL first learns a dynamics model that predicts s_{t+1}, then uses it in one of two ways: to generate extra training data (Dyna, MBPO) or to think a few steps ahead before acting (planning). The thread running through Lecture 11 of CS224R is how to avoid being dragged down by model error. Start synthetic rollouts from real states and keep them short, average errors out with an ensemble of models, and attach a value function to the tail of long-horizon plans. Whether a model is worth learning depends on whether it is easier or harder to learn than the policy.
The last guest deck in CS234 Winter 2026, by Shane Gu of Google DeepMind: 36 slides, no public recording. It has three threads. First, Solomonoff induction says the best predictor is the shortest program that generates the data, and prediction comes in three levels. Second, a forward model F and two inverse models, Π and Q, share one notation, which shows how shooting and direct collocation each plan with a different kind of model and why TDMs and Generalized Decision Transformers are world models at a different time scale. Third, the deck asks whether video models can become the foundation model for the physical world.
Lecture 5 of CMU 11-768 defines an agent's plan as an explicit, inspectable, revisable representation of intended behavior for this task, and gives four reasons to add planning structure: modularity, environment feedback, long horizons, and control. Fried's own MACU has a manager decompose tasks into a DAG and dispatch parallel sub-agents, raising Odysseys success from 8.5% to 34.0%; on an OSWorld subset, no planning scores 25.0%, an initial DAG with no revisions scores 27.8%, and allowing 10 revisions reaches 58.3%.
07-380 HW2 has three parts. The programming assignment has you write PDDL for a pancake-cooking robot, solve it optimally with unified-planning and Fast Downward, then implement RRT and RRT* in rrt.py (Q2–Q7). The written part covers GraphPlan, one LP modeling problem, and two LP graphing problems. A Gradescope online component is CMU-only. This guide covers structure, prerequisites, and running the local autograder; it contains no solutions.
07-380 Lec3 replaces propositional successor-state axioms with STRIPS actions (pre/add/del sets), which turns planning back into state-space search. When that search is too large, GraphPlan lets actions run in parallel and never deletes facts, and delete relaxation drops delete effects entirely, yielding the heuristics behind FF and Fast Downward.
Previous articles covered training from scratch and long-horizon memory. This one goes deeper: how to make the agent's 'planning' itself better? WebWeaver tackles it architecturally (dual-agent iterative outline optimization). DeepPlanner tackles it through training (advantage shaping for planning tokens). Both point to the same conclusion: planning is the ceiling of deep research.