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Introduces Go-Explore, an exploration algorithm capable of solving the grand-challenge hard-exploration Atari games Montezuma′s Revenge and Pitfall.
An ambitious goal for artificial intelligence is to create agents that behave ethically. Unfortunately, there is widespread disagreement about which ethical theory an agent should follow. This paper translates philosophical work on moral uncertainty into an RL algorithm and investigates how moral uncertainty can avoid extreme behavior compared to single theories.
Open-ended search algorithms are relatively unstudied in the field of AI safety and yet are starting to show increasing promise as a path to producing advanced AI. This paper discusses the safety implications of open-ended search algorithms in AI.
In this paper, we introduce a novel form of value function, Q(s, s′). We highlight the benefits of this approach in terms of value function transfer, learning within redundant action spaces, and learning off-policy from state observations generated by sub-optimal or completely random policies.
This work presents an exploration and imitation-learning-based agent capable of state-of-the-art performance in playing text-based computer games using the Go-Explore exploration algorithm.
The definite version of the Go-Explore algorithm. On top of the results from the original pre-print, it introduces a dynamic representation that supports all Atari games, a variant in which the exploration phase can be performed in stochastic environments, and demonstrates Go-Explore working in a robotics environment.