Optimization of parameterized policies for reinforcement learning (RL) is an important and
challenging problem in artificial intelligence. Among the most common approaches are …

Active Inference is a theory arising from theoretical neuroscience which casts action and
planning as Bayesian inference problems to be solved by minimizing a single quantity—the …

Scaling decision theoretic planning to large multiagent systems is challenging due to
uncertainty and partial observability in the environment. We focus on a multiagent planning …

The overestimation caused by function approximation is a well-known property in Q-learning
algorithms, especially in single-critic models, which leads to poor performance in practical …

Policy gradient methods have achieved remarkable successes in solving challenging
reinforcement learning problems. However, it still often suffers from the large variance issue …

Very often when studying non-equilibrium systems one is interested in analysing dynamical
behaviour that occurs with very low probability, so called rare events. In practice, since rare …

Many resource management problems require sequential decision-making under
uncertainty, where the only uncertainty affecting the decision outcomes are exogenous …

In reinforcement learning, importance sampling is a widely used method for evaluating an
expectation under the distribution of data of one policy when the data has in fact been …

We propose expected policy gradients (EPG), which unify stochastic policy gradients (SPG)
and deterministic policy gradients (DPG) for reinforcement learning. Inspired by expected …

This paper synchronizes control theory with computer vision by formalizing object tracking
as a sequential decision-making process. A reinforcement learning (RL) agent successfully …