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CNL2ASP: converting controlled natural language sentences into ASP
Answer Set Programming (ASP) is a popular declarative programming language for solving hard combinatorial problems.
DOP: Deep Optimistic Planning with Approximate Value Function Evaluation
To alleviate this problem, we present DOP, a deep model-based reinforcement learning algorithm, which exploits action values to both (1) guide the exploration of the state space and (2) plan effective policies.
Model-based Reinforcement Learning
reinforcement-learning
+1
Q-CP: Learning Action Values for Cooperative Planning
Research on multi-robot systems has demonstrated promising results in manifold applications and domains.
Learning Human-Robot Handovers Through $π$-STAM: Policy Improvement With Spatio-Temporal Affordance Maps
Human-robot handovers are characterized by high uncertainty and poor structure of the problem that make them difficult tasks.
Robotics
