30 citations · 85 across the 17 of their papers we have counts for
3 papers · 1 filter
Is Policy Learning Overrated?: Width-Based Planning and Active Learning for Atari
Benjamin Ayton, Masataro Asai
Width-based planning has shown promising results on Atari 2600 games using pixel input, while using substantially fewer environment interactions than reinforcement learning. Recent…
Reinforcement Learning for Classical Planning: Viewing Heuristics as Dense Reward Generators
Clement Gehring, Masataro Asai, Rohan Chitnis +4
Recent advances in reinforcement learning (RL) have led to a growing interest in applying RL to classical planning domains or applying classical planning methods to some complex RL…
Classical Planning in Deep Latent Space
Masataro Asai, Hiroshi Kajino, Alex Fukunaga +1
Current domain-independent, classical planners require symbolic models of the problem domain and instance as input, resulting in a knowledge acquisition bottleneck. Meanwhile, alth…