Technical Note: \cal Q-Learning
Machine Learning
Map learning with uninterpreted sensors and effectors
Artificial Intelligence
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Sparse Distributed Memory
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability
Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability
Coevolution of neural networks using a layered pareto archive
Proceedings of the 8th annual conference on Genetic and evolutionary computation
An object-oriented representation for efficient reinforcement learning
Proceedings of the 25th international conference on Machine learning
Racing the Beam: The Atari Video Computer System
Racing the Beam: The Atari Video Computer System
Measuring universal intelligence: Towards an anytime intelligence test
Artificial Intelligence
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Bandit based monte-carlo planning
ECML'06 Proceedings of the 17th European conference on Machine Learning
Automatic state abstraction from demonstration
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
HyperNEAT-GGP: a hyperNEAT-based atari general game player
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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In this article we introduce the Arcade Learning Environment (ALE): both a challenge problem and a platform and methodology for evaluating the development of general, domain-independent AI technology. ALE provides an interface to hundreds of Atari 2600 game environments, each one different, interesting, and designed to be a challenge for human players. ALE presents significant research challenges for reinforcement learning, model learning, model-based planning, imitation learning, transfer learning, and intrinsic motivation. Most importantly, it provides a rigorous testbed for evaluating and comparing approaches to these problems. We illustrate the promise of ALE by developing and benchmarking domain-independent agents designed using well-established AI techniques for both reinforcement learning and planning. In doing so, we also propose an evaluation methodology made possible by ALE, reporting empirical results on over 55 different games. All of the software, including the benchmark agents, is publicly available.