… robot as well as constraints imposed for its safe operation. In this paper we propose a
modelbased reinforcement learning approach for continuous environments with constraints. The …

… respect to time-constraints beats heuristic … self-learning agent can successfully manage
time constraints with the agent performing better than the traditional benchmark, a time-constraint …

… time constraints. … constraints: cumulative and instantaneous. In order to evaluate algorithm
performance, we briefly introduce benchmarks and metrics for constrained reinforcement learn…

… expected cumulative reward while obeying the action-based constraints. For example, the
constraints in our application limit the total number of times that PS and WE can be selected at …

… learning and planning with constrained time, however, the exploration is not active. This
paper proposes a RTP-Q reinforcement learning … environment into time constraints exploration …

… The time constraint in the title of this thesis reflects this idea; it refers to the computational
time available in between observing a sample and selecting the next action, ie, the time …

… reinforcement learning (RL) approach for a multi-product inventory control problem with different
lead times… We also develop the equivalent reinforcement learning formulation, while the …

Reinforcement learning can greatly benefit from the use of options as a way of encoding
recurring behaviours and to foster exploration. An important open problem is how can an agent …

… The reinforcement learning agent learns this policy to … with fixed time constraints using
reinforcement learning to plan … dynamic elements, and time constraints within which the nodes (…

… Reinforcement Learning (RL) algorithm to synthesise policies for an unknown Markov Decision
Process (MDP), such that a linear time … using real-time constrained optimization. In: AAAI. …