44 research outputs found
State Abstraction in MAXQ Hierarchical Reinforcement Learning
Many researchers have explored methods for hierarchical reinforcement
learning (RL) with temporal abstractions, in which abstract actions are defined
that can perform many primitive actions before terminating. However, little is
known about learning with state abstractions, in which aspects of the state
space are ignored. In previous work, we developed the MAXQ method for
hierarchical RL. In this paper, we define five conditions under which state
abstraction can be combined with the MAXQ value function decomposition. We
prove that the MAXQ-Q learning algorithm converges under these conditions and
show experimentally that state abstraction is important for the successful
application of MAXQ-Q learning.Comment: 7 pages, 2 figure
A Deep Hierarchical Approach to Lifelong Learning in Minecraft
We propose a lifelong learning system that has the ability to reuse and
transfer knowledge from one task to another while efficiently retaining the
previously learned knowledge-base. Knowledge is transferred by learning
reusable skills to solve tasks in Minecraft, a popular video game which is an
unsolved and high-dimensional lifelong learning problem. These reusable skills,
which we refer to as Deep Skill Networks, are then incorporated into our novel
Hierarchical Deep Reinforcement Learning Network (H-DRLN) architecture using
two techniques: (1) a deep skill array and (2) skill distillation, our novel
variation of policy distillation (Rusu et. al. 2015) for learning skills. Skill
distillation enables the HDRLN to efficiently retain knowledge and therefore
scale in lifelong learning, by accumulating knowledge and encapsulating
multiple reusable skills into a single distilled network. The H-DRLN exhibits
superior performance and lower learning sample complexity compared to the
regular Deep Q Network (Mnih et. al. 2015) in sub-domains of Minecraft
A Survey on Artificial Intelligence and Robotics
Today many multi-national companies or organizations are adopting the use of automation. Automation means replacing the human by intelligent robots or machines which are capable to work as human (may be better than human). Artificial intelligence is a way of making machines, robots or software to think like human. As the concept of artificial intelligence is use in robotics, it is necessary to understand the basic functions which are required for robots to think and work like human. These functions are planning, acting, monitoring, perceiving and goal reasoning. These functions help robots to develop its skills and implement it. Since robotics is a rapidly growing field from last decade, it is important to learn and improve the basic functionality of robots and make it more useful and user-friendly