Contents
What are options in reinforcement learning?
The options framework (Precup, 2000; Sutton, Precup & Singh, 1999) provides a natural way of incorporating extended actions into reinforcement learning systems. An option is specified by a set of states in which the option can be initiated, an internal policy and a termination condition.
What is reinforcement hierarchy?
In operant conditioning, a reinforcement hierarchy is a list of actions, starting with the most desirable and ending with the least desirable. A reinforcement hierarchy can be used to determine the relative frequency and desirability of different actions, and is employed when applying the Premack principle.
Why does hierarchy sometimes work so well in reinforcement learning?
Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning? Surprisingly, we find that most of the observed benefits of hierarchy can be attributed to improved exploration, as opposed to easier policy learning or imposed hierarchical structures.
How are hierarchical value functions used in reinforcement learning?
In this work, we propose a framework that integrates deep reinforcement learning with hierarchical value functions (h-DQN), where the agent is motivated to solve intrinsic goals (via learning options) to aid exploration. These goals provide for efficient exploration and help mitigate the sparse feedback problem.
How does hierarchical reinforcement learning integrate temporal abstraction?
We present hierarchical-DQN (h-DQN), a framework to integrate hierarchical value functions, operating at different temporal scales, with intrinsically motivated deep reinforcement learning.
When was the options framework introduced in RL?
The Options Framework, introduced by Sutton, Precup & Singh in 1999, provides a way to implement hierarchies and macro-actions in RL. This is probably one of the most common formulations of HRL.
How does feudal reinforcement learning ( FRL ) work?
Also, the Options Framework does not consider task segmentation explicitly. Feudal Reinforcement Learning (FRL) defines a control hierarchy, in which a level of managers can control sub-managers, while at the same time this level of managers is controlled by super-managers.