Relational Representations in Reinforcement Learning: Review and Open Problems
نویسنده
چکیده
This paper is about representation in RL. We discuss some of the concepts in representation and generalization in reinforcement learning and argue for higher-order representations, instead of the commonly used propositional representations. The paper contains a small review of current reinforcement learning systems using higher-order representations, followed by a brief discussion. The paper ends with research directions and open problems. keywords: reinforcement learning, generalization, relational representations, review
منابع مشابه
Learning in Rich Representations: Inductive Logic Programming and Computational Scientific Discovery
• Encourage more research on learning in rich representations, such as relational representations and differential equations, which can be used for modeling a variety of real world problems. Inductive logic programming (ILP) is concerned with learning from data and domain knowledge in relational representations. ILP started off by addressing the task of learning logic programs from examples and...
متن کاملRelational Reinforcement Learning for Agents in Worlds with Objects
In reinforcement learning, an agent tries to learn a policy, i.e., how to select an action in a given state of the environment, so that it maximizes the total amount of reward it receives when interacting with the environment. We argue that a relational representation of states is natural and useful when the environment is complex and involves many interrelated objects. Relational reinforcement...
متن کاملInverse Reinforcement Learning in Relational Domains
In this work, we introduce the first approach to the Inverse Reinforcement Learning (IRL) problem in relational domains. IRL has been used to recover a more compact representation of the expert policy leading to better generalization performances among different contexts. On the other hand, relational learning allows representing problems with a varying number of objects (potentially infinite),...
متن کاملLearning with Whom to Communicate Using Relational Reinforcement Learning
Relational reinforcement learning is a promising direction within reinforcement learning research. It upgrades reinforcement learning techniques by using relational representations for states, actions, and learned value-functions or policies to allow natural representations and abstractions of complex tasks. Multiagent systems are characterized by their relational structure and present a good e...
متن کاملReinforcement Learning in Neural Networks: A Survey
In recent years, researches on reinforcement learning (RL) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. Neural network reinforcement learning (NNRL) is among the most popular algorithms in the RL framework. The advantage of using neural networks enables the RL to search for optimal policies more efficiently in several real-life applicat...
متن کامل