233,338 research outputs found
Deep Reinforcement Learning in Decision-Making of Autonomous Driving: A Survey
Deep reinforcement learning (DRL) is a burgeoning sub-field in the realm of artificial intelligence that combines the benefits of deep learning (DL) and reinforcement learning (RL). By integrating these two methods, deep reinforcement learning has effectively addressed previously complex problems related to autonomous driving system (ADs) and has played a vital role in their development. Specifically, deep learning enhances reinforcement learning’s ability to handle extensive high-dimensional data, which is critical for ADs. In this review, we mainly concentrate on the application of DRL algorithms in ADs, focusing primarily on decision-making processes. The review will be- gin by introducing deep learning and reinforcement learning independently before delving into the current applications, future prospects, and challenges facing deep reinforcement learning in this field. Finally, we will conclude with a summary of this review
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Towards Informed Exploration for Deep Reinforcement Learning
In this thesis, we discuss various techniques for improving exploration for deep reinforcement learning. We begin with a brief review of reinforcement learning (RL) and the fundamental v.s. exploitation trade-off. Then we review how deep RL has improved upon classical and summarize six categories of the latest exploration methods for deep RL, in the order increasing usage of prior information. We then explore representative works in three categories discuss their strengths and weaknesses. The first category, represented by Soft Q-learning, uses regularization to encourage exploration. The second category, represented by count-based via hashing, maps states to hash codes for counting and assigns higher exploration to less-encountered states. The third category utilizes hierarchy and is represented by modular architecture for RL agents to play StarCraft II. Finally, we conclude that exploration by prior knowledge is a promising research direction and suggest topics of potentially impact
Causal Reinforcement Learning: A Survey
Reinforcement learning is an essential paradigm for solving sequential
decision problems under uncertainty. Despite many remarkable achievements in
recent decades, applying reinforcement learning methods in the real world
remains challenging. One of the main obstacles is that reinforcement learning
agents lack a fundamental understanding of the world and must therefore learn
from scratch through numerous trial-and-error interactions. They may also face
challenges in providing explanations for their decisions and generalizing the
acquired knowledge. Causality, however, offers a notable advantage as it can
formalize knowledge in a systematic manner and leverage invariance for
effective knowledge transfer. This has led to the emergence of causal
reinforcement learning, a subfield of reinforcement learning that seeks to
enhance existing algorithms by incorporating causal relationships into the
learning process. In this survey, we comprehensively review the literature on
causal reinforcement learning. We first introduce the basic concepts of
causality and reinforcement learning, and then explain how causality can
address core challenges in non-causal reinforcement learning. We categorize and
systematically review existing causal reinforcement learning approaches based
on their target problems and methodologies. Finally, we outline open issues and
future directions in this emerging field.Comment: 48 pages, 10 figure
An introduction to reinforcement learning for neuroscience
Reinforcement learning has a rich history in neuroscience, from early work on
dopamine as a reward prediction error signal for temporal difference learning
(Schultz et al., 1997) to recent work suggesting that dopamine could implement
a form of 'distributional reinforcement learning' popularized in deep learning
(Dabney et al., 2020). Throughout this literature, there has been a tight link
between theoretical advances in reinforcement learning and neuroscientific
experiments and findings. As a result, the theories describing our experimental
data have become increasingly complex and difficult to navigate. In this
review, we cover the basic theory underlying classical work in reinforcement
learning and build up to an introductory overview of methods used in modern
deep reinforcement learning that have found applications in systems
neuroscience. We start with an overview of the reinforcement learning problem
and classical temporal difference algorithms, followed by a discussion of
'model-free' and 'model-based' reinforcement learning together with methods
such as DYNA and successor representations that fall in between these two
categories. Throughout these sections, we highlight the close parallels between
the machine learning methods and related work in both experimental and
theoretical neuroscience. We then provide an introduction to deep reinforcement
learning with examples of how these methods have been used to model different
learning phenomena in the systems neuroscience literature, such as
meta-reinforcement learning (Wang et al., 2018) and distributional
reinforcement learning (Dabney et al., 2020). Code that implements the methods
discussed in this work and generates the figures is also provided.Comment: Code available at:
https://colab.research.google.com/drive/1kWOz2Uxn0cf2c4YizqIXQKWyxeYd6wvL?usp=sharin
Towards a Theoretical Foundation of Policy Optimization for Learning Control Policies
Gradient-based methods have been widely used for system design and
optimization in diverse application domains. Recently, there has been a renewed
interest in studying theoretical properties of these methods in the context of
control and reinforcement learning. This article surveys some of the recent
developments on policy optimization, a gradient-based iterative approach for
feedback control synthesis, popularized by successes of reinforcement learning.
We take an interdisciplinary perspective in our exposition that connects
control theory, reinforcement learning, and large-scale optimization. We review
a number of recently-developed theoretical results on the optimization
landscape, global convergence, and sample complexity of gradient-based methods
for various continuous control problems such as the linear quadratic regulator
(LQR), control, risk-sensitive control, linear quadratic
Gaussian (LQG) control, and output feedback synthesis. In conjunction with
these optimization results, we also discuss how direct policy optimization
handles stability and robustness concerns in learning-based control, two main
desiderata in control engineering. We conclude the survey by pointing out
several challenges and opportunities at the intersection of learning and
control.Comment: To Appear in Annual Review of Control, Robotics, and Autonomous
System
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