SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4951–5000 of 15113 papers

TitleStatusHype
Asking the Difficult Questions: Goal-Oriented Visual Question Generation via Intermediate Rewards—0
A Sliding-Window Algorithm for Markov Decision Processes with Arbitrarily Changing Rewards and Transitions—0
A Socially Aware Reinforcement Learning Agent for The Single Track Road Problem—0
Aspect and Opinion Aware Abstractive Review Summarization with Reinforced Hard Typed Decoder—0
Aspect-based Sentiment Classification via Reinforcement Learning—0
A Spiking Binary Neuron -- Detector of Causal Links—0
A Spiking Neural Network Learning Markov Chain—0
A Spiking Neural Network Structure Implementing Reinforcement Learning—0
ASPiRe:Adaptive Skill Priors for Reinforcement Learning—0
Transferable Cost-Aware Security Policy Implementation for Malware Detection Using Deep Reinforcement Learning—0
ASQ-IT: Interactive Explanations for Reinforcement-Learning Agents—0
Assembly robots with optimized control stiffness through reinforcement learning—0
Assessing and Accelerating Coverage in Deep Reinforcement Learning—0
Assessing Deep Reinforcement Learning Policies via Natural Corruptions at the Edge of Imperceptibility—0
Assessing Evolutionary Terrain Generation Methods for Curriculum Reinforcement Learning—0
Assessing Generalization in TD methods for Deep Reinforcement Learning—0
Assessing Human Interaction in Virtual Reality With Continually Learning Prediction Agents Based on Reinforcement Learning Algorithms: A Pilot Study—0
Assessing Policy, Loss and Planning Combinations in Reinforcement Learning using a New Modular Architecture—0
Assessing the Impact of Distribution Shift on Reinforcement Learning Performance—0
Assessing the Zero-Shot Capabilities of LLMs for Action Evaluation in RL—0
Assessing Transferability from Simulation to Reality for Reinforcement Learning—0
Assessment of Reinforcement Learning Algorithms for Nuclear Power Plant Fuel Optimization—0
Assessment of Reward Functions in Reinforcement Learning for Multi-Modal Urban Traffic Control under Real-World limitations—0
Associative Memory Based Experience Replay for Deep Reinforcement Learning—0
Assume-Guarantee Reinforcement Learning—0
Assured Learning-enabled Autonomy: A Metacognitive Reinforcement Learning Framework—0
Assured RL: Reinforcement Learning with Almost Sure Constraints—0
A stabilizing reinforcement learning approach for sampled systems with partially unknown models—0
A State Aggregation Approach for Solving Knapsack Problem with Deep Reinforcement Learning—0
A State Augmentation based approach to Reinforcement Learning from Human Preferences—0
A State Representation Dueling Network for Deep Reinforcement Learning—0
A State Representation for Diminishing Rewards—0
A statistical learning strategy for closed-loop control of fluid flows—0
A Stochastic Composite Augmented Lagrangian Method For Reinforcement Learning—0
A physics-informed reinforcement learning approach for the interfacial area transport in two-phase flow—0
A Strong Baseline for Batch Imitation Learning—0
A Structure-aware Online Learning Algorithm for Markov Decision Processes—0
A Study of AI Population Dynamics with Million-agent Reinforcement Learning—0
A Study of Continual Learning Methods for Q-Learning—0
A study of first-passage time minimization via Q-learning in heated gridworlds—0
A Study of State Aliasing in Structured Prediction with RNNs—0
A Study on Dense and Sparse (Visual) Rewards in Robot Policy Learning—0
A Subgame Perfect Equilibrium Reinforcement Learning Approach to Time-inconsistent Problems—0
A Succinct Summary of Reinforcement Learning—0
A SUMO Framework for Deep Reinforcement Learning Experiments Solving Electric Vehicle Charging Dispatching Problem—0
A Surrogate-Assisted Controller for Expensive Evolutionary Reinforcement Learning—0
A survey of benchmarking frameworks for reinforcement learning—0
A Survey of Constraint Formulations in Safe Reinforcement Learning—0
A Survey of Continual Reinforcement Learning—0
A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified