Induction and Exploitation of Subgoal Automata for Reinforcement Learning

Subgoal Discovery for Hierarchical Reinforcement Learning Using Learned Policies

Reinforcement learning addresses the problem of learning to select actions in order to maximize an agent’s performance in unknown environments. To scale reinforcement learning to complex real-world tasks, agent must be able to discover hierarchical structures within their learning and control systems. This paper presents a method by which a reinforcement learning agent can discover subgoals wit...

Hierarchical Reinforcement Learning Based on Subgoal Discovery and Subpolicy Specialization

A method is described for hierarchical reinforcement learning. High-level policies automatically discover subgoals; low-level policies learn to specialize for different subgoals. Subgoals are represented as desired abstract observations which cluster raw input data. Experiments shows that this method outperforms several flat methods. The full paper can be found in [1]. Introduction. The promise...

development and implementation of an optimized control strategy for induction machine in an electric vehicle

in the area of automotive engineering there is a tendency to more electrification of power train. in this work control of an induction machine for the application of electric vehicle is investigated. through the changing operating point of the machine, adapting the rotor magnetization current seems to be useful to increase the machines efficiency. in the literature there are many approaches wh...

Exploration and Exploitation in Reinforcement Learning

Accelerating Action Dependent Hierarchical Reinforcement Learning Through Autonomous Subgoal Discovery

This paper presents a new method for the autonomous construction of hierarchical action and state representations in reinforcement learning, aimed at accelerating learning and extending the scope of such systems. In this approach, the agent uses information acquired while learning one task to discover subgoals for similar tasks by analyzing the learned policy using Monte Carlo sampling. The age...