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Showing 1 to 20 of 25 for “"Proximal Policy Optimization"”.
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Preferential proximal policy optimization in reinforcement learning
The Proximal Policy Optimization (PPO), a policy gradient method, excels in reinforcement learning with its ”surrogate” objective function and stochastic gradient ascent. However, PPO does not fully consider the significance of frequently encountered states in policy/value updates. To address this, …
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Low-thrust Spacecraft guidance and control using proximal policy optimization
… use of the Reinforcement Learning (RL) algorithm Proximal Policy Optimization (PPO) for solving low-thrust spacecraft guidance and control problems. First, an agent is trained to complete a 302 day mass-optimal low-thrust transfer between the Earth and Mars. This is accomplished while only …
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Machine learning for high performance computing applications
… system, and reinforcement learning using proximal policy optimization. Included are three different advancements applying these techniques. The first application used K-means clustering and Gradient Boosted Tree Regression (GBTR) to predict estimated queue time for jobs submitted to an HPC …
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Reinforcement Learning for Autonomous Aircraft Control and Aerial Maneuvering in Simulated Environments
… navigation in a Unity-based simulation. Using Proximal Policy Optimization (PPO), the agent learns to fly, avoid obstacles, and reach targets through continuous control. A combination of environment design and reward shaping was essential for achieving stable learning. Results show that …
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Winning at Pokémon Random Battles Using Reinforcement Learning
… 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) on the official Pokémon Showdown gen4randombattles ladder, which is the best known performance by any non-human agent for this format. …
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Selecting appropriate reinforcement-learning algorithms for robot manipulation domains
… Evolution Strategy, Deep Deterministic Policy Gradients, and Proximal Policy Optimization. We compare their performance on various target domains to measure quantitatively their dependence on varied features of the environment. We study the effect of actuation noise, observation noise, …
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Reinforcement Learning–Based Discrete Prompt Optimization for Neuro-Symbolic Structured Simplification of Complex Game Descriptions with Large Language Models
… formalizes simplification as a discrete prompt optimization problem and introduces a neuro-symbolic pipeline that maps raw natural language into controlled GameChangineer sentences via scenario normalization, retrieval-augmented code generation, and AST-based FACTS extraction. A reinforcement …
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Market making in dry waters : reinforcement learning strategies for market making in illiquid markets
… (DQN), Advantage Actor-Critic (A2C) and Proximal Policy Optimization (PPO). They are evaluated in a simulated stock market environment on their performance in liquid and illiquid market conditions. Findings show that DQN outperforms the other in both conditions. The research highlights …
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Hybrid AI-driven Approach to Context-Aware Inter-Slice Load Balancing for Cloud-Native Functions in 5G Networks
… (MADRL) models like deep Q-networks (DQN) and proximal policy optimization (PPO) for traffic load balancing across core network slices, enhancing service quality. Kubernetes-integrated deployments use Prometheus for real-time predictive analytics, expanding beyond network-centric metrics to …
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Predicting Chemical Reactions at the MechanisticLevel through Deep Reinforcement Learning
… we build a graph neural network-based policy and value network, where the policy network is first pre-trained on an open database of elementary radical reactions (RMechDB). Then, we use proximal policy optimization (PPO) to fine-tune the model and predict reasonable reaction mechanisms …
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Reinforcement Learning, Modeling Markets, and Professional Basketball Free Agency
… (RL) environment is developed, leveraging Proximal Policy Optimization (PPO) to approximate optimal policies for team decision-making.</p> <p>Empirical results demonstrate that the RL agent successfully learns strategic bidding behavior that aligns with dynamic programming benchmarks in …
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Adversarial Prompt Transformation for Systematic Jailbreaks of LLMs
… into a successful jailbreak. Thus it learns a policy based on relation to existing jailbreak prompts that informs the generator LLM of what makes an adversarial prompt successful. This was implemented using Proximal Policy Optimization (PPO) and tested with both a classifier and judge reward …
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ACADIA: Efficient and Robust Adversarial Attacks Against Deep Reinforcement Learning
… algorithms, Deep-Q Learning Network (DQN) and Proximal Policy Optimization (PPO), under Atari games and MuJoCo where both targeted and non-targeted attacks are considered with or without the state-of-the-art defenses in DRL (i.e., RADIAL and ATLA). Our results demonstrate that the proposed …
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Computational Simulation and Machine Learning for Quality Improvement in Composites Assembly
… interact with novel implementations of modified proximal policy optimization, based on a newly developed reinforcement learning algorithm. The resulting reinforcement learning agents are able to successfully address the underlying optimization problems that underpin the process and quality …
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SPIRAL: Iterative Subgraph Expansion for Knowledge-Graph Based Retrieval-Augmented Generation
… previous work in its use of a trained, iterative policy network built on top of a prior over triples, delivering improved performance on multi-hop question answering tasks. Stage 1 trains a single-label GLASS-GNN on shortest-path heuristics, producing frozen, question-aware node embeddings at …
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Deep Reinforcement Learning for Multirotor Flight Control: A Comparative Study of Sim-to-Real Training and Real-World Performance
… on factors that most influence sim-to-real policy transfer. Using the Proximal Policy Optimization (PPO) algorithm, extensive ablation studies evaluated the effects of domain randomization, reward weighting, observation representation, and neural network architecture. Policies were trained …
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Engineering Design Automation via Imitation Learning and Reinforcement Learning
… for surrogate design tasks like aircraft design optimization. Additionally, we assess the performance of RL agents, specifically Proximal Policy Optimization and Advantage Actor-Critic ,in the same design task. Both RL approaches achieved higher Q-scores (up to 0.99) but incurred significant …
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The Application of Reinforcement Learning for Interceptor Guidance
… upon traditional guidance methods. Specifically, proximal policy optimization (PPO) was selected as the reinforcement learning algorithm due to its advanced and efficient nature, as well as its successful use in related work. A framework was developed and tuned for the interceptor guidance …
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Orbital Maneuvers and Interplanetary Trajectory Design via Reinforcement Learning
… of reinforcement learning (RL) to the design and optimization of low-thrust spacecraft trajectories, with an emphasis on autonomy, adaptability, and robustness in the presence of system uncertainties and unmodeled perturbations. Classical approaches to low-thrust trajectory design are …
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Assessment of Reinforcement Learning Algorithms for Nuclear Power Plant Fuel Optimization
The nuclear fuel loading pattern optimization problem belongs to the class of large-scale combinatorial optimization and has been studied since the dawn of the commercial nuclear energy industry. It is also characterized by multiple objectives and constraints, which makes it impossible to solve …
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