text: This paper presents a nearest-neighbor based continuous control policy. Two algorithms are presented: NN-1 runs open-loop trajectories from the beginning state, and NN-2 runs a state-condition policy that retrieves nearest state-action tuples for each state. The overall algorithm is very simple to implement and can do reasonably well on some simple control tasks, but quickly gets overwhelmed by higher-dimensional and stochastic environments. It is very similar to "Learning to Steer on Winding Tracks Using Semi-Parametric Control Policies" and is effectively an indirect form of tile coding (each could be seen as a fixed voronoi cell). I am sure this idea has been tried before in the 90s but I am not familiar enough with all the literature to find it (A quick google search brings this up: Reinforcement Learning of Active Recognition Behaviors, with a chapter on nearest-neighbor lookup for policies: https://people.eecs.berkeley.edu/~trevor/papers/1997-045/node3.html). Although I believe there is work to be done in the current round of RL research using nearest neighbor policies, I don't believe this paper delves very far into pushing new ideas (even a simple adaptive distance metric could have provided some interesting results, nevermind doing a learned metric in a latent space to allow for rapid retrainig of a policy on new domains....), and for that reason I don't think it has a place as a conference paper at ICLR. I would suggest its submission to a workshop where it might have more use triggering discussion of further work in this area.