Goal Recognition as Reinforcement Learning

Automatic Goal Generation for Reinforcement Learning Agents

Reinforcement learning is a powerful technique to train an agent to perform a task. However, an agent that is trained using reinforcement learning is only capable of achieving the single task that is specified via its reward function. Such an approach does not scale well to settings in which an agent needs to perform a diverse set of tasks, such as navigating to varying positions in a room or m...

Perceptual Goal Specifications for Reinforcement Learning

Hierarchical extreme learning machine based reinforcement learning for goal localization

The objective of goal localization is to find the location of goals in noisy environments. Simple actions are performed to move the agent towards the goal. The goal detector should be capable of minimizing the error between the predicted locations and the true ones. Few regions need to be processed by the agent to reduce the computational effort and increase the speed of convergence. In this pa...

Transfer Learning for Reinforcement Learning through Goal and Policy Parameterization

Relational reinforcement learning has allowed results from reinforcement learning tasks to be re-used in other, closely related, tasks. This transfer of knowledge is made possible by the use of parameters in the representations of the task-description and the learned policy. In this paper, we will give a description of the current state of the art of transfer learning with relational reinforcem...

Transfer Learning for Reinforcement Learning through Goal- and Policy Parametrization

Relational reinforcement learning has allowed results from reinforcement learning tasks to be re-used in other, closely related, tasks. This transfer of knowledge is made possible by the use of parameters in the representations of the task-description and the learned policy. In this paper, we will give a description of the current state of the art of transfer learning with relational reinforcem...