First return, then explore
Reinforcement learning promises to solve complex sequential-decision problems
autonomously by specifying a high-level reward function only. However, reinforcement …
autonomously by specifying a high-level reward function only. However, reinforcement …
Go-explore: a new approach for hard-exploration problems
A grand challenge in reinforcement learning is intelligent exploration, especially when
rewards are sparse or deceptive. Two Atari games serve as benchmarks for such hard …
rewards are sparse or deceptive. Two Atari games serve as benchmarks for such hard …
Revisiting the arcade learning environment: Evaluation protocols and open problems for general agents
The Arcade Learning Environment (ALE) is an evaluation platform that poses the challenge
of building AI agents with general competency across dozens of Atari 2600 games. It …
of building AI agents with general competency across dozens of Atari 2600 games. It …
[LIVRE][B] Artificial intelligence and games
GN Yannakakis, J Togelius - 2018 - Springer
Georgios N. Yannakakis Julian Togelius Page 1 Artificial Intelligence and Games Georgios N.
Yannakakis Julian Togelius Page 2 Artificial Intelligence and Games Page 3 Georgios N …
Yannakakis Julian Togelius Page 2 Artificial Intelligence and Games Page 3 Georgios N …
A survey of algorithms for black-box safety validation of cyber-physical systems
Autonomous cyber-physical systems (CPS) can improve safety and efficiency for safety-
critical applications, but require rigorous testing before deployment. The complexity of these …
critical applications, but require rigorous testing before deployment. The complexity of these …
The benchmark lottery
The world of empirical machine learning (ML) strongly relies on benchmarks in order to
determine the relative effectiveness of different algorithms and methods. This paper …
determine the relative effectiveness of different algorithms and methods. This paper …
[LIVRE][B] Distributional reinforcement learning
The first comprehensive guide to distributional reinforcement learning, providing a new
mathematical formalism for thinking about decisions from a probabilistic perspective …
mathematical formalism for thinking about decisions from a probabilistic perspective …
State of the art control of atari games using shallow reinforcement learning
The recently introduced Deep Q-Networks (DQN) algorithm has gained attention as one of
the first successful combinations of deep neural networks and reinforcement learning. Its …
the first successful combinations of deep neural networks and reinforcement learning. Its …
Best-first width search: Exploration and exploitation in classical planning
It has been shown recently that the performance of greedy best-first search (GBFS) for
computing plans that are not necessarily optimal can be improved by adding forms of …
computing plans that are not necessarily optimal can be improved by adding forms of …
Model-free, model-based, and general intelligence
H Geffner - arxiv preprint arxiv:1806.02308, 2018 - arxiv.org
During the 60s and 70s, AI researchers explored intuitions about intelligence by writing
programs that displayed intelligent behavior. Many good ideas came out from this work but …
programs that displayed intelligent behavior. Many good ideas came out from this work but …