This paper examines the suitability of Lego Mindstorms robotic kits as a platform for teaching the concepts of reinforcement learning. The reinforcement learning algorithm Sarsa was implemented on board an autonomous Mindstorms robot, and applied to two learning tasks. The reasons behind the differing results obtained on these two tasks are discussed, and several issues related to the suitab...

Reinforcement learning is a machine learning area that studies which actions an agent can take in order to optimize a cumulative reward function. Recently, a new class of reinforcement learning algorithms with multiple, possibly conflicting, reward functions was proposed. We call this class of algorithms the multi-objective reinforcement learning (MORL) paradigm. We give an overview on multi-ob...

Reinforcement learning is a general formalism for sequential decision-making, with recent algorithm development focusing on function approximation to handle large state spaces and high-dimensional, high-velocity (sensor) data. The success of function approximators, however, hinges on the quality of the data representation. In this work, we explore representation learning within batch reinforcem...

A key goal of AI is to create lifelong learning agents that can leverage prior experience to improve performance on later tasks. In reinforcement learning problems, one way to summarize prior experience for future use is through options, which are behaviorally extended actions (subpolicies) for how to behave. Options can then be used to potentially accelerate learning in new reinforcement learn...

The search for interpretable reinforcement learning policies is of high academic and industrial interest. Especially for industrial systems, domain experts are more likely to deploy autonomously learned controllers if they are understandable and convenient to evaluate. Basic algebraic equations are supposed to meet these requirements, as long as they are restricted to an adequate complexity. He...

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 ...

This paper shows that the distributed representation found in Learning Vector Quantization (LVQ) enables reinforcement learning methods to cope with a large decision search space, defined in terms of equivalence classes of input patterns like those found in the game of Go. In particular, this paper describes S[arsa]LVQ, a novel reinforcement learning algorithm and shows its feasibility for patt...

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 ...