Abstract Monte Carlo Tree Search (MCTS) is a powerful approach to designing game-
playing bots or solving sequential decision problems. The method relies on intelligent tree …

Unavoidable dead-ends are common in many probabilistic planning problems, eg when
actions may fail or when operating under resource constraints. An important objective in …

Liste der Autoren Page 1 Liste der Autoren Clemens Beckstein Gerhard Brewka Christian
Borgelt Wolfram Burgard Hans-Dieter Burkhard Stephan Busemann Thomas Christaller Leonie …

Dynamic programming is a well-known approach for solving MDPs. In large state spaces,
asynchronous versions like Real-Time Dynamic Programming have been applied …

Abstract A Relational Markov Decision Process (RMDP) is a first-order representation to
express all instances of a single probabilistic planning domain with possibly unbounded …

Reinforcement learning in problems with symbolic state spaces is challenging due to the
need for reasoning over long horizons. This paper presents a new approach that utilizes …

Neural planners for RDDL MDPs produce deep reactive policies in an offline fashion. These
scale well with large domains, but are sample inefficient and time-consuming to train from …

We present pyRDDLGym, a Python framework for auto-generation of OpenAI Gym
environments from RDDL declerative description. The discrete time step evolution of …

Abstract Monte-Carlo Tree Search (MCTS) methods, such as Upper Confidence Bound
applied to Trees (UCT), are instrumental to automated planning techniques. However, UCT …

Probabilistic planning is very useful for handling uncertainty in planning tasks to be carried
out by robots. ROSPlan is a framework for task planning in the Robot Operating System …