Optimal Control via Reinforcement Learning with Symbolic Policy Approximation
نویسندگان
چکیده
منابع مشابه
Reinforcement Learning via AIXI Approximation
This paper introduces a principled approach for the design of a scalable general reinforcement learning agent. This approach is based on a direct approximation of AIXI, a Bayesian optimality notion for general reinforcement learning agents. Previously, it has been unclear whether the theory of AIXI could motivate the design of practical algorithms. We answer this hitherto open question in the a...
متن کاملReinforcement Learning Policy Approximation by Behavior Trees
Traditionally a Reinforcement Learning (RL) policy is stored in a lookup table. From such a table it is difficult to observe the behavioral logic or manually adjust this logic post-learning is difficult. This paper shows how behavioral logic of a RL controller is presented in an insightful manner and can be adjusted using the Behavior Tree (BT) framework. It shows a method to approximate an RL ...
متن کاملPolicy Gradient Methods for Reinforcement Learning with Function Approximation
Function approximation is essential to reinforcement learning, but the standard approach of approximating a value function and determining a policy from it has so far proven theoretically intractable. In this paper we explore an alternative approach in which the policy is explicitly represented by its own function approximator, independent of the value function, and is updated according to the ...
متن کاملOptimal policy switching algorithms for reinforcement learning
We address the problem of single-agent, autonomous sequential decision making. We assume that some controllers or behavior policies are given as prior knowledge, and the task of the agent is to learn how to switch between these policies. We formulate the problem using the framework of reinforcement learning and options (Sutton, Precup & Singh, 1999; Precup, 2000). We derive gradient-based algor...
متن کاملGuided Cost Learning: Deep Inverse Optimal Control via Policy Optimization
Reinforcement learning can acquire tcomplex behaviors from high-level specifications. However, defining a cost function that can be optimized effectively and encodes the correct task is challenging in practice. We explore how inverse optimal control (IOC) can be used to learn behaviors from demonstrations, with applications to torque control of high-dimensional robotic systems. Our method addre...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IFAC-PapersOnLine
سال: 2017
ISSN: 2405-8963
DOI: 10.1016/j.ifacol.2017.08.805