Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 189 for “"deep reinforcement learning"”.
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Deep reinforcement learning for quadrupeds
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-12-04 without embargo terms
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Deep reinforcement learning in RoboCup Keepaway
The central theme in multi-agent reinforcement learning often involves the coordination of agents to accomplish a common task. However, in complex environ- ments, the agent action space scales exponentially with the number of agents. As a result of this complexity, the coordination graph formalism …
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Playing Tetris with deep reinforcement learning
… cleared in an average game. In recent years, deep reinforcement learning (DRL) has achieved outstanding performance with Atari and Go games. An initial attempt by Stevens and Pradhan (2016) to use deep reinforcement learning to play Tetris was unsuccessful. The objective of this thesis is to …
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DEEP REINFORCEMENT LEARNING FOR BUILDING ENERGY MANAGEMENT
… residents. Multiple studies have shown that Deep Reinforcement Learning has great potential in controlling energy allocation in buildings. This thesis aims to demonstrate the use of PPO, a recent Deep Reinforce- ment Learning algorithm with an actor-critic framework and Trust Region Policy, …
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Toward efficient multi-agent deep reinforcement learning
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms
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Leveraging Engineering Expertise in Deep Reinforcement Learning
Deep reinforcement learning has been used to craft robust and performant control policies for legged robotics. However, the engineering processes to create these policies are often plagued by long training times that slow down engineering iteration. This thesis suggests that model-based controllers …
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Fault Tolerant Deep Reinforcement Learning for Aerospace Applications
… making, and many more. In this thesis, a deep-learning-based control system is designed with fault-tolerant and disturbance rejection capabilities and applied to a high-order nonlinear dynamic system. The approach uses a Reinforcement Learning architecture that combines concepts from …
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DEEP REINFORCEMENT LEARNING FOR ROBOT-ASSISTED SURGICAL TRAINING
… of trainee surgeons is extended. To shorten the learning curve of hand-eye coordination in the laparoscopic surgery, guidance from a Deep Reinforcement Learning (DRL) intelligent agent is proposed to support the trainee in the surgical training. In this thesis, a Cyber-Physical System (CPS) is …
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Improving cache replacement policy using deep reinforcement learning
This thesis explores the use of reinforcement learning approaches to improve replacement policies of caches. In today's internet, caches play a vital role in improving performance of data transfers and load speeds. From video streaming to information retrieval from databases, caches allow …
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Sample-Efficient Deep Reinforcement Learning for Continuous Control
Reinforcement learning (RL) is a powerful, generic approach to discovering optimal policies in complex sequential decision-making problems. Recently, with flexible function approximators such as neural networks, RL has greatly expanded its realm of applications, from playing computer games with …
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Addressing deep reinforcement learning: empirical algorithm performance evaluations∗
Due to the rapidly paced production of deep reinforcement learning (RL) research papers, some recent publications have begun to critique the manner in which RL algorithm performances are evaluated. Building on this recent scrutiny, our work attempts to identify the precise aspects of empirical deep …
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Physically Constrained PCB Placement Using Deep Reinforcement Learning
This thesis provides an in depth exploration of Reinforcement Learning (RL) based PCB component placement with emphasis on physically verified placements. Unlike prior methods that rely on heuristic proxies for placement quality, this work focuses entirely on routing based metrics that result in …
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Automatic knobs-tuning for DB2 using deep reinforcement learning
… knob configuration for IBM DB2. BLUTune utilizes deep reinforcement learning and features a unique transfer-learning approach to training which allows for fast learning. In experimental validation, BLUTune demonstrates its capability of producing effective configurations across differing sizes of …
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Addressing stale gradients in asynchronous federated deep reinforcement learning
Advancements in reinforcement learning (RL) via deep neural networks have enabled their application to a variety of real-world problems. However, these applications often suffer from long training times. While attempts to distribute training have been successful in controlled scenarios, they face …
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Robot Navigation in Cluttered Environments with Deep Reinforcement Learning
… a wealth of challenges. This thesis proposes a deep reinforcement learning based system that determines collision free navigation robot velocities directly from a sequence of depth images and a desired direction of travel. The system is designed such that a real robot could be placed in an …
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Aligning Language Models Using Multi-Objective Deep Reinforcement Learning
… One of the dominant alignment techniques is reinforcement learning from human feedback (RLHF). RLHF aims to optimize one objective based on human preferences. However, the cost of high-quality human feedback is enormous. Having all human annotators consistent in their opinions on desirable …
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Deep reinforcement learning on 1-layer circuit routing problem
… the long-term reward. The recent development of deep learning, especially deep reinforcement learning, can be a good approach to finding better ordering on attacking the routing problem. We introduce a reinforcement learning approach to the traditional 2-point nets in 1-layer maze routing problem.
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End-to-end deep reinforcement learning in computer systems
… based on environmental cues. In this context, reinforcement learning (RL) methods have since their inception appealed to systems developers. They promise to acquire complex decision policies from raw feedback signals. Despite their conceptual popularity, RL methods are scarcely found in …
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Superconducting qubit readout pulse optimization using deep reinforcement learning
… a new pulse shaping optimization module using deep reinforcement learning. Compared to conventional readout methods in a simulated environment, we are able to reduce required readout pulse lengths by over 63% in single-qubit environments and by over 57% in multi-qubit environments. In addition …
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Mobile Robot Obstacle Avoidance based on Deep Reinforcement Learning
… collision avoidance algorithm is trained via Deep Reinforcement Learning, which enables the robot to learn by itself from its experiences, and then fit a mathematical model by updating the parameters of a neural network. The trained neural network architecture is capable of choosing an action …
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