Temporal-difference (TD) learning is an important field in reinforcement learning. Sarsa and Q-Learning are among the most used TD algorithms. The Q(σ) algorithm (Sutton and Barto (2017)) unifies both. This paper extends the Q(σ) algorithm to an online multi-step algorithm Q(σ, λ) using eligibility traces and introduces Double Q(σ) as the extension of Q(σ) to double learning. Experiments sugges...

A behavioral economic approach to alcohol use disorders (AUDs) emphasizes both individual and environmental determinants of alcohol use. The current study examined individual differences in alcohol demand (i.e., motivation for alcohol under escalating conditions of price) and delayed reward discounting (i.e., preference for immediate small rewards compared to delayed larger rewards) in 61 heavy...

The aim of this paper is to enhance the performance of a reinforcement learning game agent controller, within a dynamic game environment, through the retention of learned information over a series of consecutive games. Using a variation of the classic arcade game Pac-Man, the Sarsa algorithm has been utilised for the control of the Pac-Man game agent. The results indicate the use of stateaction...

In the multi-sensor activity recognition domain, the input space is often large and contains irrelevant and overlapped features. It is important to perform feature selection in order to select the smallest number of features which can describe the outputs. This paper proposes a new feature selection algorithms using the maximal relevance and maximal complementary criteria (MRMC) based on neural...