Algorithms for evolutionary computation, which simulate the process of natural selection to solve optimization problems, are an effective tool for discovering high-performing reinforcement-learning policies. Because they can automatically find good representations, handle continuous action spaces, and cope with partial observability, evolutionary reinforcement-learning approaches have a strong ...

This paper discusses why traditional reinforcement learning methods, and algorithms applied to those models, result in poor performance in situated domains characterized by multiple goals, noisy state, and inconsistent reinforcement. We propose a methodology for designing reinforcement functions that take advantage of implicit domain knowledge in order to accelerate learning in such domains. Th...

Planning for autonomous vehicles is a challenging process that involves navigating through dynamic and unpredictable surroundings while making judgments in real-time. Traditional planning methods sometimes rely on predetermined rules or customized heuristics, which could not generalize well to various driving conditions. In this article, we provide unique framework enhance vehicle by fusing con...

This paper introduces Dex, a reinforcement learning environment toolkit specialized for training and evaluation of continual learning methods as well as general reinforcement learning problems. We also present the novel continual learning method of incremental learning, where a challenging environment is solved using optimal weight initialization learned from first solving a similar easier envi...

In the Spoken Dialogue System literature, all studies consider the dialogue move as the unquestionable unit for reinforcement learning. Rather than learning at the dialogue move level, we apply the learning at the design level for three reasons : 1/ to alleviate the high-skill prerequisite for developers, 2/ to reduce the learning complexity by taking into account just the relevant subset of th...

Several recent efforts in the field of reinforcement learning have focused attention on the importance of regularization, but the techniques for incorporating regularization into reinforcement learning algorithms, and the effects of these changes upon the convergence of these algorithms, are ongoing areas of research. In particular, little has been written about the use of regularization in onl...

ing Reusable Cases from Reinforcement Learning Andreas von Hessling and Ashok K. Goel College of Computing Georgia Institute of Technology Atlanta, GA 30318 {avh, goel}@cc.gatech.edu Abstract. Reinforcement Learning is a popular technique for gameplaying because it can learn an optimal policy for sequential decision problems in which the outcome (or reward) is delayed. However, Reinforcement Le...

The aim of this work is to combine three successful AI techniques –Reinforcement Learning (RL), Heuristics Search and Case Based Reasoning (CBR)– creating a new algorithm that allows the use of cases in a case base as heuristics to speed up Reinforcement Learning algorithms. This approach, called Case Based Heuristically Accelerated Reinforcement Learning (CB-HARL), builds upon an emerging tech...

The DRL (Delayed Reinforcement Learning) problem is classical in Reinforcement Learning theory. There were several agent architectures solving that problem including some connectionist architectures. This work describes an early connectionist agent architecture, the CAA architecture, that solved the problem using the concept of emotion it its learning rule. The architecture is compared to anoth...

Learning Classifier Systems use reinforcement learning, evolutionary computing and/or heuristics to develop adaptive systems. This paper extends the ZCS Learning Classifier System to improve its internal modelling capabilities. Initially, results are presented which show performance in a traditional reinforcement learning task incorporating lookahead within the rule structure. Then a mechanism ...