Guiding a Reinforcement Learner with Natural Language Advice: Initial Results in RoboCup Soccer
نویسندگان
چکیده
We describe our current efforts towards creating a reinforcement learner that learns both from reinforcements provided by its environment and from human-generated advice. Our research involves two complementary components: (a) mapping advice expressed in English to a formal advice language and (b) using advice expressed in a formal notation in a reinforcement learner. We use a subtask of the challenging RoboCup simulated soccer task (Noda et al. 1998) as our testbed.
منابع مشابه
Skill Acquisition Via Transfer Learning and Advice Taking
We describe a reinforcement learning system that transfers skills from a previously learned source task to a related target task. The system uses inductive logic programming to analyze experience in the source task, and transfers rules for when to take actions. The target task learner accepts these rules through an advice-taking algorithm, which allows learners to benefit from outside guidance ...
متن کاملGiving Advice about Preferred Actions to Reinforcement Learners Via Knowledge-Based Kernel Regression
We present a novel formulation for providing advice to a reinforcement learner that employs supportvector regression as its function approximator. Our new method extends a recent advice-giving technique, called Knowledge-Based Kernel Regression (KBKR), that accepts advice concerning a single action of a reinforcement learner. In KBKR, users can say that in some set of states, an action’s value ...
متن کاملTransfer Learning via Advice Taking
The goal of transfer learning is to speed up learning in a new task by transferring knowledge from one or more related source tasks. We describe a transfer method in which a reinforcement learner analyzes its experience in the source task and learns rules to use as advice in the target task. The rules, which are learned via inductive logic programming, describe the conditions under which an act...
متن کاملA Task Specification Language for Bootstrap Learning
Reinforcement learning (RL) is an effective framework for online learning by autonomous agents. Most RL research focuses on domain-independent learning algorithms, requiring an expert human to define the environment (state and action representation) and task to be performed (e.g. start state and reward function) on a case-by-case basis. In this paper, we describe a general language for a teache...
متن کاملA General Purpose Task Specification Language for Bootstrap Learning Technical Report UT-AI-08-1
Reinforcement learning (RL) is an effective framework for online learning by autonomous agents. Most RL research focuses on domain-independent learning algorithms, requiring an expert human to define the environment (state and action representation) and task to be performed (e.g. start state and reward function) on a case-by-case basis. In this paper, we describe a general language for a teache...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2004