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We propose new width-based planning and learning algorithms inspired from acareful analysis of the design decisions made by previous width-based planners.The algorithms are applied over the Atari-2600 games and our best performingalgorithm, Novelty guided Critical Path Learning (N-CPL), outperforms thepreviously introduced width-based planning and learning algorithms $\pi$-IW(1),$\pi$-IW(1)+ and $\pi$-HIW(n, 1). Furthermore, we present a taxonomy of theAtari-2600 games according to some of their defining characteristics. Thisanalysis of the games provides further insight into the behaviour andperformance of the algorithms introduced. Namely, for games with largebranching factors, and games with sparse meaningful rewards, N-CPL outperforms$\pi$-IW, $\pi$-IW(1)+ and $\pi$-HIW(n, 1).

Width-based Lookaheads with Learnt Base Policies and Heuristics Over the Atari-2600 Benchmark