Add to ORKGModel-based reinforcement learning methods make efficient use of samples by building a model of the environment and planning with it. Compared to model-free methods, they usually take fewer samples to converge to the optimal policy. Despite that efficiency, model-based methods may not learn the optimal policy due to structural modeling assumptions. In this thesis, we show that by combining model- based methods with hierarchically optimal recursive Q-learning (HORDQ) under a hierarchical reinforcement learning framework, the proposed approach learns the optimal policy even when the assumptions of the model are not all satisfied. The effectiveness of our approach is demonstrated with the Bus domain and Infinite Mario – a Java implementation o...
This thesis focuses on reinforcement learning (RL) which is a machine learning paradigm under which ...
Hierarchical task decompositions play an essential role in the design of com-plex simulation and dec...
Two common approaches to sequential decision-making are AI planning (AIP) and reinforcement learning...
Model-based reinforcement learning methods make efficient use of samples by building a model of the ...
Factored representations, model-based learning, and hierarchies are well-studied techniques for impr...
Deep reinforcement learning methods are capable of learning complex heuristics starting with no prio...
The graphical models paradigm provides a general framework for automatically learning hierarchical m...
This paper presents a new approach to hierarchical reinforcement learning based on the MAXQ decompos...
Model-free reinforcement learning methods have successfully been applied to practical applications s...
Hierarchical task decompositions play an essential role in the design of complex simulation and deci...
Search based planners such as A* and Dijkstra\u27s algorithm are proven methods for guiding today\u2...
We propose a model-based approach to hierarchical reinforcement learning that exploits shared knowle...
In this paper we present a hybrid system combining techniques from symbolic planning and reinforceme...
This thesis studies the problem of learning a model in Model-Based Reinforcement Learning (MBRL). We...
Model-based reinforcement learning algorithms tend to achieve higher sample efficiency than model-fr...
This thesis focuses on reinforcement learning (RL) which is a machine learning paradigm under which ...
Hierarchical task decompositions play an essential role in the design of com-plex simulation and dec...
Two common approaches to sequential decision-making are AI planning (AIP) and reinforcement learning...
Model-based reinforcement learning methods make efficient use of samples by building a model of the ...
Factored representations, model-based learning, and hierarchies are well-studied techniques for impr...
Deep reinforcement learning methods are capable of learning complex heuristics starting with no prio...
The graphical models paradigm provides a general framework for automatically learning hierarchical m...
This paper presents a new approach to hierarchical reinforcement learning based on the MAXQ decompos...
Model-free reinforcement learning methods have successfully been applied to practical applications s...
Hierarchical task decompositions play an essential role in the design of complex simulation and deci...
Search based planners such as A* and Dijkstra\u27s algorithm are proven methods for guiding today\u2...
We propose a model-based approach to hierarchical reinforcement learning that exploits shared knowle...
In this paper we present a hybrid system combining techniques from symbolic planning and reinforceme...
This thesis studies the problem of learning a model in Model-Based Reinforcement Learning (MBRL). We...
Model-based reinforcement learning algorithms tend to achieve higher sample efficiency than model-fr...
This thesis focuses on reinforcement learning (RL) which is a machine learning paradigm under which ...
Hierarchical task decompositions play an essential role in the design of com-plex simulation and dec...
Two common approaches to sequential decision-making are AI planning (AIP) and reinforcement learning...