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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=reinforcement+learning">
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  <title><![CDATA[UMBC ebiquity RSS Tag Search]]></title>
  <link><![CDATA[http://ebiquity.umbc.edu//tags/html/?t=reinforcement+learning]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for reinforcement learning]]></description>
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      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/386/Prediction-markets-for-fun-feedback-and-the-future"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/301/Feature-Based-Local-Policy-Reinforcement-Learning"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/235/Using-Expert-Traces-to-Reduce-Training-Time-in-Reinforcement-Learning"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/204/Transfer-in-the-Context-of-Reinforcement-Learning-by-Mapping-Q-Tables"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/5/META-LEVEL-CONTROL-IN-BOUNDED-RATIONAL-AGENTS"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1189/Enhancing-Trustworthiness-in-LLM-Generated-Code-A-Reinforcement-Learning-and-Domain-Knowledge-Constrained-Approach"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1132/Knowledge-Graphs-and-Reinforcement-Learning-A-Hybrid-Approach-for-Cybersecurity-Problems"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1053/Offline-RL-CKG-A-hybrid-AI-model-for-cybersecurity-tasks"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1047/Knowledge-guided-Two-player-Reinforcement-Learning-for-Cyber-Attacks-and-Defenses"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/960/Using-Knowledge-Graphs-and-Reinforcement-Learning-for-Malware-Analysis"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/897/Reinforcement-Quantum-Annealing-A-Hybrid-Quantum-Learning-Automata"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/880/Reinforcement-Quantum-Annealing-A-Quantum-Assisted-Learning-Automata-Approach"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/383/Understanding-Malware-Behavior-with-Reinforcement-Learning"/>
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 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/386/Prediction-markets-for-fun-feedback-and-the-future">
  <title><![CDATA[Prediction markets for fun, feedback and the future]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/386/Prediction-markets-for-fun-feedback-and-the-future</link>
  <description><![CDATA[Prediction markets, when they work well, solve a fundamental problem: how to aggregate individual beliefs into a meaningful quantitative estimate of the probability that a given event will occur. They also provide incentives for people to disseminate privately-held information. I will describe one way to help these markets work better: incorporating a learning agent who provides liquidity, called a market maker. Along the way, the design of this agent raises and solves some fundamental proble...]]></description>
  <dc:date>2011-03-17</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/301/Feature-Based-Local-Policy-Reinforcement-Learning">
  <title><![CDATA[Feature-Based Local Policy Reinforcement Learning]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/301/Feature-Based-Local-Policy-Reinforcement-Learning</link>
  <description><![CDATA[MS Defense

The problem of learning to control an agent in an arbitrary environment is
difficult. In robotics, the standard approach is to hand-code and manually
fine-tune a robot's perception of its environment and the actions it should
take given its current state. This is both time-consuming and expensive. A
better approach is to learn features and action policies without significant
manual intervention. This problem is investigated in the context of learning
image features to con...]]></description>
  <dc:date>2009-05-12</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/235/Using-Expert-Traces-to-Reduce-Training-Time-in-Reinforcement-Learning">
  <title><![CDATA[Using Expert Traces to Reduce Training Time in Reinforcement Learning]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/235/Using-Expert-Traces-to-Reduce-Training-Time-in-Reinforcement-Learning</link>
  <description><![CDATA[Reinforcement learning algorithms are typically used to solve Markov
decision problems.  Using reinforcement learning to find an optimal or
sub-optimal solution is a slow process.  The use of prior knowledge can
shorten the length of this task.  In this thesis, we explore the use of
expert traces to speed up learning.  An expert trace is a record of the
states visited and actions taken by an expert in a given domain, which is
then used to reduce the training time of reinforcement learni...]]></description>
  <dc:date>2008-04-21</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/204/Transfer-in-the-Context-of-Reinforcement-Learning-by-Mapping-Q-Tables">
  <title><![CDATA[Transfer in the Context of Reinforcement Learning by Mapping Q-Tables]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/204/Transfer-in-the-Context-of-Reinforcement-Learning-by-Mapping-Q-Tables</link>
  <description><![CDATA[Transfer in machine learning is the process of using knowledge
learned in a source domain to speed learning in one or more related
target domains. In human learning, transfer is a ubiquitous
phenomenon. In machine learning, transfer is far less common. In
this thesis we present a transfer method for reinforcement learning
when the learner does not have access to a model of either the
source or the target domains (i.e., the transition and reward
probabilities are unknown) and there is n...]]></description>
  <dc:date>2007-05-02</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/5/META-LEVEL-CONTROL-IN-BOUNDED-RATIONAL-AGENTS">
  <title><![CDATA[META-LEVEL CONTROL IN BOUNDED-RATIONAL AGENTS]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/5/META-LEVEL-CONTROL-IN-BOUNDED-RATIONAL-AGENTS</link>
  <description><![CDATA[Autonomous agents must make real-time decisions on the scheduling and
coordination of domain activities. These control decisions are made in
the context of limited resources and uncertainty about action
outcomes. The meta-level control problem is deciding how to sequence
domain and control actions without consuming too many resources in the
process. The state-of-the-art in agent architectures and control
algorithms does not explicitly reason about the cost of time and other
resources...]]></description>
  <dc:date>2003-12-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1189/Enhancing-Trustworthiness-in-LLM-Generated-Code-A-Reinforcement-Learning-and-Domain-Knowledge-Constrained-Approach">
  <title><![CDATA[Enhancing Trustworthiness in LLM Generated Code: A Reinforcement Learning and Domain-Knowledge Constrained Approach]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1189/Enhancing-Trustworthiness-in-LLM-Generated-Code-A-Reinforcement-Learning-and-Domain-Knowledge-Constrained-Approach</link>
  <description><![CDATA[Imagine analyzing a piece of code that uses the function ConnectToServer() with an encrypted string as its argument. A large language model (LLM), trained on extensive programming data, might flag the use of encryption as suspicious and generate an explanation suggesting that the function likely connects to a malicious server. While this explanation might seem plausible, it can often be unfaithful—it overgeneralizes from statistical patterns in its training data without truly understanding ...]]></description>
  <dc:date>2025-02-25</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1132/Knowledge-Graphs-and-Reinforcement-Learning-A-Hybrid-Approach-for-Cybersecurity-Problems">
  <title><![CDATA[Knowledge Graphs and Reinforcement Learning: A Hybrid Approach for Cybersecurity Problems]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1132/Knowledge-Graphs-and-Reinforcement-Learning-A-Hybrid-Approach-for-Cybersecurity-Problems</link>
  <description><![CDATA[With the explosion of available data and computational power, machine learning and deep learning techniques are being increasingly used to solve problems. The domain of cybersecurity is no exception, as we have seen multiple papers in the recent past using data-driven machine learning approaches for different tasks.

Rule-based and supervised machine learning-based approaches are often brittle in detecting attacks, can be defeated by adversaries that adapt, and cannot use the knowledge of e...]]></description>
  <dc:date>2023-07-19</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1053/Offline-RL-CKG-A-hybrid-AI-model-for-cybersecurity-tasks">
  <title><![CDATA[Offline RL+CKG: A hybrid AI model for cybersecurity tasks]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1053/Offline-RL-CKG-A-hybrid-AI-model-for-cybersecurity-tasks</link>
  <description><![CDATA[AI models for cybersecurity have to detect and defend against constantly evolving cyber threats. Much effort is spent building defenses for zero days and unseen variants of known cyber-attacks. Current AI models for cybersecurity struggle with these yet unseen threats due to the constantly evolving nature of threat vectors, vulnerabilities, and exploits. This paper shows that cybersecurity AI models will be improved and more general if we include semi-structured representations of background ...]]></description>
  <dc:date>2023-03-27</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1047/Knowledge-guided-Two-player-Reinforcement-Learning-for-Cyber-Attacks-and-Defenses">
  <title><![CDATA[Knowledge guided Two-player Reinforcement Learning for Cyber Attacks and Defenses]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1047/Knowledge-guided-Two-player-Reinforcement-Learning-for-Cyber-Attacks-and-Defenses</link>
  <description><![CDATA[Cyber defense exercises are an important avenue to understand the technical capacity of organizations when faced with cyber threats. Information derived from these exercises often leads to finding unseen methods to exploit vulnerabilities in an organization. These often lead to better defense mechanisms that can counter previously unknown exploits. With recent developments in cyber battle simulation platforms, we can generate a defense exercise environment and train reinforcement learning (RL...]]></description>
  <dc:date>2022-12-12</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/960/Using-Knowledge-Graphs-and-Reinforcement-Learning-for-Malware-Analysis">
  <title><![CDATA[Using Knowledge Graphs and Reinforcement Learning for Malware Analysis]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/960/Using-Knowledge-Graphs-and-Reinforcement-Learning-for-Malware-Analysis</link>
  <description><![CDATA[Machine learning algorithms used to detect attacks are limited by the fact that they cannot incorporate the background knowledge that an analyst has. This limits their suitability in detecting new attacks. Reinforcement learning is different from traditional machine learning algorithms used in the cybersecurity domain. Compared to traditional ML algorithms, reinforcement learning does not need a mapping of the input-output space or a specific user-defined metric to compare data points. This i...]]></description>
  <dc:date>2020-12-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/897/Reinforcement-Quantum-Annealing-A-Hybrid-Quantum-Learning-Automata">
  <title><![CDATA[Reinforcement Quantum Annealing: A Hybrid Quantum Learning Automata]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/897/Reinforcement-Quantum-Annealing-A-Hybrid-Quantum-Learning-Automata</link>
  <description><![CDATA[We introduce the notion of reinforcement quantum annealing (RQA) scheme in which an intelligent
agent searches in the space of Hamiltonians and interacts with a quantum annealer that plays the
stochastic environment role of learning automata. At each iteration of RQA, after analyzing results
(samples) from the previous iteration, the agent adjusts the penalty of unsatisfied constraints and
re-casts the given problem to a new Ising Hamiltonian. As a proof-of-concept, we propose a novel
ap...]]></description>
  <dc:date>2020-05-14</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/880/Reinforcement-Quantum-Annealing-A-Quantum-Assisted-Learning-Automata-Approach">
  <title><![CDATA[Reinforcement Quantum Annealing: A Quantum-Assisted Learning Automata Approach]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/880/Reinforcement-Quantum-Annealing-A-Quantum-Assisted-Learning-Automata-Approach</link>
  <description><![CDATA[Superseded by.:  Ramin Ayanzadeh, Milton Halem, and Tim Finin, Reinforcement Quantum Annealing: A Hybrid Quantum Learning Automata, Nature Scientific Reports, v10, n1, May 2020.




We introduce the reinforcement quantum annealing (RQA) scheme in which an intelligent agent interacts with a quantum annealer that plays the stochastic environment role of learning automata and tries to iteratively find better Ising Hamiltonians for the given problem of interest. As a proof-of-concept, we pro...]]></description>
  <dc:date>2020-01-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/383/Understanding-Malware-Behavior-with-Reinforcement-Learning">
  <title><![CDATA[Understanding Malware Behavior with Reinforcement Learning]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/383/Understanding-Malware-Behavior-with-Reinforcement-Learning</link>
  <description><![CDATA[Presentation given at the OnRamp II Symposium, 13-14 October 2021.

This work was funded, in part, by a grant from the NSA through the On-Ramp program
and by the National Science Foundation under Grant Number 2114892.]]></description>
  <dc:date>2021-10-14</dc:date>
 </item>
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