Add to ORKGWithin the field of Reinforcement Learning (RL) the successful application of abstraction can play a huge role in decreasing the time required for agents to learn competent policies. Many examples of this speed-up have been observed throughout the literature. Reward Shaping is one such technique for utilising abstractions in this way. This thesis focuses on how an agent can learn its own abstractions from its own experiences to be used for Potential Based Reward Shaping. As the thesis progresses, the environments for which the abstraction construction is automated grow in complexity and scope --- while also utilising less external knowledge of the domains. This culminates in the approaches \textit{Uniform Property State Abstraction} (UPSA...
One major limitation to the applicability of Reinforcement Learning (RL) to many practical domains i...
Reinforcement Learning (RL) is a subset of machine learning primarily concerned with goal-directed l...
Recent advances in reinforcement-learning research have demonstrated impressive results in building ...
The application of reinforcement learning (RL) algorithms is often hindered by the combinatorial exp...
Thesis (Ph.D.)--University of Washington, 2021Rapid strides made in the development of computing inf...
In reinforcement learning (RL), an agent learns to solve a task by interacting with its environment....
One of the fundamental problems in Artificial Intelligence is sequential decision making in a flexib...
Deep reinforcement learning is rapidly gaining attention due to recent successes in a variety of pro...
Reward shaping (RS) is a powerful method in reinforcement learning (RL) for overcoming the problem o...
Reward shaping is an efficient way to incorporate domain knowledge into a reinforcement learning age...
The field of artificial intelligence (AI) is devoted to the creation of artificial decision-makers t...
Reinforcement learning (RL) models the learning process of humans, but as exciting advances are made...
This thesis presents novel work on how to improve exploration in reinforcement learning using domain...
Reinforcement learning (RL) aims to learn optimal behaviors for agents to maximize cumulative reward...
Reinforcement Learning (RL) is an elegant approach to tackle sequential decision-making problems. In...
One major limitation to the applicability of Reinforcement Learning (RL) to many practical domains i...
Reinforcement Learning (RL) is a subset of machine learning primarily concerned with goal-directed l...
Recent advances in reinforcement-learning research have demonstrated impressive results in building ...
The application of reinforcement learning (RL) algorithms is often hindered by the combinatorial exp...
Thesis (Ph.D.)--University of Washington, 2021Rapid strides made in the development of computing inf...
In reinforcement learning (RL), an agent learns to solve a task by interacting with its environment....
One of the fundamental problems in Artificial Intelligence is sequential decision making in a flexib...
Deep reinforcement learning is rapidly gaining attention due to recent successes in a variety of pro...
Reward shaping (RS) is a powerful method in reinforcement learning (RL) for overcoming the problem o...
Reward shaping is an efficient way to incorporate domain knowledge into a reinforcement learning age...
The field of artificial intelligence (AI) is devoted to the creation of artificial decision-makers t...
Reinforcement learning (RL) models the learning process of humans, but as exciting advances are made...
This thesis presents novel work on how to improve exploration in reinforcement learning using domain...
Reinforcement learning (RL) aims to learn optimal behaviors for agents to maximize cumulative reward...
Reinforcement Learning (RL) is an elegant approach to tackle sequential decision-making problems. In...
One major limitation to the applicability of Reinforcement Learning (RL) to many practical domains i...
Reinforcement Learning (RL) is a subset of machine learning primarily concerned with goal-directed l...
Recent advances in reinforcement-learning research have demonstrated impressive results in building ...