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AAMAS
2002
Springer
13 years 3 months ago
Relational 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 ...
Saso Dzeroski
KES
1998
Springer
13 years 7 months ago
An acquisition of the relation between vision and action using self-organizing map and reinforcement learning
An agent must acquire internal representation appropriate for its task, environment, sensors. As a learning algorithm, reinforcement learning is often utilized to acquire the rela...
Kazunori Terada, Hideaki Takeda, Toyoaki Nishida
ILP
2003
Springer
13 years 8 months ago
Graph Kernels and Gaussian Processes for Relational Reinforcement Learning
RRL is a relational reinforcement learning system based on Q-learning in relational state-action spaces. It aims to enable agents to learn how to act in an environment that has no ...
Thomas Gärtner, Kurt Driessens, Jan Ramon
IJCAI
2007
13 years 5 months ago
Utile Distinctions for Relational Reinforcement Learning
We introduce an approach to autonomously creating state space abstractions for an online reinforcement learning agent using a relational representation. Our approach uses a tree-b...
William Dabney, Amy McGovern
ICML
2005
IEEE
14 years 4 months ago
Dynamic preferences in multi-criteria reinforcement learning
The current framework of reinforcement learning is based on maximizing the expected returns based on scalar rewards. But in many real world situations, tradeoffs must be made amon...
Sriraam Natarajan, Prasad Tadepalli