This paper proposes DeepSynth, a method for effective training of deep Reinforcement Learning (RL) agents when the reward is sparse and non-Markovian, but at same time progress towards requires achieving an unknown sequence high-level objectives. Our employs novel algorithm synthesis compact automata to uncover this sequential structure automatically. We synthesise human-interpretable automaton...

Résumé : This paper addresses a relational reinforcement learning-like problem with general action-model relational learning and planning techniques. We propose an integrated system for both action model learning and action selection in the context of adaptive behavior of autonomous agents. Learning is incremental. It operates with relational representations and produces disjunctions of 1st ord...

This paper addresses the problem of introducing learning capabilities in industrial handcrafted automata-based Spoken Dialogue Systems, in order to help the developer to cope with his dialogue strategies design tasks. While classical reinforcement learning algorithms position their learning at the dialogue move level, the fundamental idea behind our approach is to learn at a finer internal deci...

The issue of finding fuzzy models with an interpretability as good as possible without decreasing the accuracy is one of the main research topics on genetic fuzzy systems. When they are used to perform on-line reinforcement learning by means of Michigan-style fuzzy classifier systems, this issue becomes even more difficult. Indeed, rule generalization (description of state-action relationships ...

Reinforcement learning is an adaptive process in which an animal utilizes its previous experience to improve the outcomes of future choices. Computational theories of reinforcement learning play a central role in the newly emerging areas of neuroeconomics and decision neuroscience. In this framework, actions are chosen according to their value functions, which describe how much future reward is...

This paper shows that a system with two link arm can obtain arm reaching movement to a target object by combination of reinforcement learning and dynamic self organizing map. Proposed model in this paper present state and action space of reinforcement learning with dynamis self organizing maps. Because these spaces are continuous. proposed model uses two dynamic self-organizing maps (DSOM) to e...

Reinforcement learning addresses the problem of learning to select actions in order to maximize one’s performance in unknown environments. To scale reinforcement learning to complex real-world tasks, such as typically studied in AI, one must ultimately be able to discover the structure in the world, in order to abstract away the myriad of details and to operate in more tractable problem spaces....

Action Selection schemes, when translated into precise algorithms, typically involve considerable design eeort and tuning of parameters. Little work has been done on solving the problem using learning. This paper compares eight diierent methods of solving the action selection problem using Reinforcement Learning (learning from rewards). The methods range from centralised and cooperative to dece...