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 38 for “"Partial observability"”.
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Computationally Efficient Reinforcement Learning under Partial Observability
… to fully observe the latent state of the system. Partially observable Markov decision processes (POMDPs) are a generalization of Markov decision processes (MDPs) that model this challenge. Unfortunately, planning and learning near-optimal policies in POMDPs is computationally intractable. Most …
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Reasoning under partial observability in heterogeneous networked systems
… declarative queries and evaluated soundly under partial observability. The same representation supports semantic minimization, allowing complex systems to be reduced to smaller models that preserve behaviors of interest. A prototype implementation demonstrates that conditional-table–based …
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Artificial intelligence in co-operative games with partial observability
… Intelligence in co-operative games that feature Partial Observability. Most video games feature a combination of both co-operation, as well as Partial Observability. Co-operative games are games that feature a team of at least two agents, that must achieve a shared goal of some kind. Partial …
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Self-attention policy architectures for reinforcement learning under partial observability
… their robustness under conditions of high partial observability on different rein-forcement learning benchmark tasks, and explore the advantages and disadvantages offered by our solution over conventional policy architectures. Additionally, we propose a novel hard attention mechanism, used …
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Deep Memory Models and Efficient Reinforcement Learning under Partial Observability
Reinforcement learning is a framework for optimal decision making that considers the long-term consequences of actions. Deep variants of reinforcement learning have emerged as powerful tools for decision making in complex environments. Prior work has mastered complex games like Go and Atari, …
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Generalizable Long-Horizon Robotic Manipulation under Uncertainty and Partial Observability
… accomplish complex, long-horizon tasks in novel, partially observable environments. In these scenarios, agents must effectively reason about uncertainty, generalize from limited experiences, and proactively plan actions to acquire missing information. This thesis tackles these core challenges by …
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A Language and Logic for Programming and Reasoning with Partial Observability
Computer systems are increasingly deployed in partially-observable environments, in which the system cannot directly determine the environment’s state but receives partial information from observations. When such a computer system executes, it risks forming an incorrect belief about the true state …
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eMARLIN: Addressing Coordination and Partial Observability in Distributed Reinforcement Learning for Traffic Signal Control
… limitations in distributive communication, and partial observability due to limited detection. This thesis addresses these challenges through a combination of analytical studies, software engineering, and methodology development. First, it identifies common ground in ATSC research by unifying …
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An integrated performance model learning and planning approach for optimal infrastructure facility maintenance under partial observability
… maintenance decision making is a stochastic and partially observable problem. This thesis presents a learning and decision-making approach for developing optimal joint inspection and maintenance policies for civil infrastructure facilities under performance model uncertainty and partially …
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Exploiting Reward Machines with Deep Reinforcement Learning in Continuous Action Domains
… challenges when solving real-world problems: partial observability and learning efficiency. In this thesis, we address these two challenges and extend previous work. First, we use reward machines to address the problem of partial observability. Then, we focus on finding the existing …
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Adaptive Abstractions for Robust Hierarchical Manipulation Planning
… of long-horizon robotic manipulation under partial observability. Tasks such as gearbox assembly or tidying a workstation involve many objects and necessitate a variety of manipulation capabilities. These long-horizon tasks are commonly addressed by hierarchical approaches, which introduce …
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Imperfect Monitoring in Multi-agent Opportunistic ChannelAccess
… consider two sources of imperfect information: partial observability and sensing errors. Partial observability models SUs that are unable to distinguish the activity of PUs from SUs. Therefore, under the partial observability models, SUs can only observe whether a channel was occupied or not …
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Resource allocation problems in stochastic sequential decision making
… makers (agents) with two main features 1) Partial observability Each agent may not have complete information regarding the system 2) Limited Communication - Each agent may not be able to communicate with all other agents at all times. We formulate a Markov Decision Process (MDP) for this …
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Winning at Pokémon Random Battles Using Reinforcement Learning
… to the massive state space, stochasticity, and partial observability. We demonstrate an agent which employs a Monte Carlo Tree Search informed by a actor-critic network trained using Proximal Policy Optimization with experience collected through self-play. The agent peaked at rank 8 (1693 Elo) …
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Explanations for Autonomous Agents
… for humans. For autonomous agents, privacy and partial observability can introduce a notion of subjective unfairness in decentralised systems. We show that this effect can also be mitigated with the use of effective explanations. In like manner, we look at Reinforcement Learning (RL) agents and …
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Beating the world's best at Super Smash Bros. with deep reinforcement learning
… The SSBM environment has complex dynamics and partial observability, making it challenging for man and machine alike. The multiplayer aspect poses an additional challenge, as the vast majority of recent advances in RL have focused on single-agent environments. Nonetheless, we will show that it …
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Visibility-Aware Navigation Among Movable Obstacles
… of them on complex manipulation problems with partial observability.
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Understanding Bonus-Based Exploration in Reinforcement Learning
… controllable dynamics. Furthermore, we find that partial observability can hinder exploration by setting up "curiosity traps" that agents can fall into. Finally, we investigate how various implementation details such as reward design and generation affect an agent’s overall performance.
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Importance sampling for reinforcement learning with multiple objectives
… market-maker, we find the sparsity of data, the partial observability of the domain, and the multiple objectives of the agent to cause serious problems for existing reinforcement learning algorithms. We employ importance sampling (likelihood ratios) to achieve good performance in partially …
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Autonomous adaptation and collaboration of unmanned vehicles for tracking submerged contacts
… the benefits of adaptive motion in overcoming partial observability of sensor observations. We focus on the challenge of target tracking with range-only measurements, relying on adaptive motion to localize and track maneuvering targets. Our primary contribution has been to develop new MOOS-IvP …
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