We present a probabilistic logic programming framework to reinforcement learning, by integrating reinforcement learning, in POMDP environments, with normal hybrid probabilistic logic programs with probabilistic answer set semantics, that is capable of representing domain-specific knowledge. We formally prove the correctness of our approach. We show that the complexity of finding a policy for a ...

For several classes of reinforcement learning schemes, convergence to action profiles that are not Nash equilibria may occur with positive probability under certain conditions on the payoff function. In this paper, we explore how an alternative reinforcement learning scheme, where the strategy of each agent is also perturbed by a strategy-dependent perturbation (or mutations) function, may excl...

This paper presents a model of motivation in learning agents to achieve adaptive, multi-task learning in complex, dynamic environments. Previously, computational models of motivation have been considered as speed-up or attention focus mechanisms for planning and reinforcement learning systems, however these different models do not provide a unified approach to the development or evaluation of c...

In this paper several multiagent reinforcement learning algorithms are investigated, compared and analyzed. An effective reinforcement learning algorithm based on non Markov environment is proposed. This algorithm uses linear programming to find the best-response policy, and avoids solving multiple Nash equilibria problem. The algorithm involves simple procedures and easy computations, and can ...

Abstract: Self-balancing control is the basis for applications of two-wheeled robots. In order to improve the self-balancing of twowheeled robots, we propose a hierarchical reinforcement learning algorithm for controlling the balance of two-wheeled robots. After describing the subgoals of hierarchical reinforcement learning, we extract features for subgoals, define a feature value vector and it...

0957-4174/$ see front matter 2013 Elsevier Ltd. A http://dx.doi.org/10.1016/j.eswa.2013.01.035 ⇑ Corresponding author. Tel.: +1 514 577 9759. E-mail addresses: [email protected] (A.H.R. K (R. Sabourin), [email protected] (F. Gagnon). This paper introduces a novel multi-agent multi-state reinforcement learning exploration scheme for dynamic spectrum access and dynamic spectrum sharing ...

The problem investigated in this paper is that of driving a car-like robot along a race track and the use of reinforcement learning to find a good control function. The reinforcement learner uses a case-based function approximator to extend the reinforcement learning paradigm to handle continuous states. The learned controller performs similar to the best control functions in both simulation an...

Off-policy deep reinforcement learning algorithms commonly compensate for overestimation bias during temporal-difference by utilizing pessimistic estimates of the expected target returns. In this work, we propose Generalized Pessimism Learning (GPL), a strategy employing novel learnable penalty to enact such pessimism. particular, learn alongside critic with dual TD-learning, new procedure esti...

This thesis presents a novel form of learning by reinforcement. Existing reinforcement learning algorithms rely on the provision of external reward signals to drive the learning algorithm. This new algorithm relies on reinforcing signals generated internally within the algorithm. The algorithm, SRS/E, described here generates expectancies ( -hypotheses), each of which gives rise to a specific p...

This paper proposes a reinforcement fuzzy adaptive learning control network (RFALCON), constructed by integrating two fuzzy adaptive learning control networks (FALCON), each of which has a feedforward multilayer network and is developed for the realization of a fuzzy controller. One FALCON performs as a critic network (fuzzy predictor), the other as an action network (fuzzy controller). Using t...