Add to ORKGState representation for intelligent agents is a continuous challenge as the need for abstraction is unavoidable in large state spaces. Pre-dictive representations offer one way to obtain state abstraction by replacing a state with a set of predictions about future interaction
Reinforcement Learning (RL) is a learning framework for modelling an agent and its\ud interaction wi...
This thesis is about how to represent and solve decision problems in Bayesian decision the ory (e.g...
We show that states of a dynamical system can be usefully represented by multi-step, action-conditio...
Temporal difference networks (or TD-Nets) offer a framework for predictive state representations. TD...
Developing general purpose algorithms for learning an accurate model of dynamical systems from examp...
We present a generalization of temporal-difference networks to include temporally abstract options o...
Automatically constructing novel representations of tasks from analysis of state spaces is a longsta...
Temporal-difference (TD) networks are a formalism for expressing and learning grounded world knowled...
AbstractThis paper is about how to represent and solve decision problems in Bayesian decision theory...
We present a novel approach to enrich classification trees with the representation learning ability ...
A Conditional Simple Temporal Network (CSTN) augments a Simple Temporal Network (STN) to include spe...
Reinforcement Learning (RL) is a learning framework for modelling an agent and its interaction with ...
We introduce a generalization of temporal-difference (TD) learning to networks of interrelated predi...
The traditional model of sequential decision making, for instance, in extensive form games, is a tre...
This thesis is about how to represent and solve decision problems in Bayesian decision theory (e.g. ...
Reinforcement Learning (RL) is a learning framework for modelling an agent and its\ud interaction wi...
This thesis is about how to represent and solve decision problems in Bayesian decision the ory (e.g...
We show that states of a dynamical system can be usefully represented by multi-step, action-conditio...
Temporal difference networks (or TD-Nets) offer a framework for predictive state representations. TD...
Developing general purpose algorithms for learning an accurate model of dynamical systems from examp...
We present a generalization of temporal-difference networks to include temporally abstract options o...
Automatically constructing novel representations of tasks from analysis of state spaces is a longsta...
Temporal-difference (TD) networks are a formalism for expressing and learning grounded world knowled...
AbstractThis paper is about how to represent and solve decision problems in Bayesian decision theory...
We present a novel approach to enrich classification trees with the representation learning ability ...
A Conditional Simple Temporal Network (CSTN) augments a Simple Temporal Network (STN) to include spe...
Reinforcement Learning (RL) is a learning framework for modelling an agent and its interaction with ...
We introduce a generalization of temporal-difference (TD) learning to networks of interrelated predi...
The traditional model of sequential decision making, for instance, in extensive form games, is a tre...
This thesis is about how to represent and solve decision problems in Bayesian decision theory (e.g. ...
Reinforcement Learning (RL) is a learning framework for modelling an agent and its\ud interaction wi...
This thesis is about how to represent and solve decision problems in Bayesian decision the ory (e.g...
We show that states of a dynamical system can be usefully represented by multi-step, action-conditio...