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 4376–4400 of 15113 papers

TitleStatusHype
Adversarially Trained Weighted Actor-Critic for Safe Offline Reinforcement Learning—0
Regularized Parameter Uncertainty for Improving Generalization in Reinforcement Learning—0
POCE: Primal Policy Optimization with Conservative Estimation for Multi-constraint Offline Reinforcement LearningCode0
Personalized Dynamic Pricing Policy for Electric Vehicles: Reinforcement learning approach—0
Training Diffusion Models Towards Diverse Image Generation with Reinforcement Learning—0
Tight Finite Time Bounds of Two-Time-Scale Linear Stochastic Approximation with Markovian Noise—0
Laboratory Experiments of Model-based Reinforcement Learning for Adaptive Optics ControlCode0
Causal State Distillation for Explainable Reinforcement LearningCode0
Design Space Exploration of Approximate Computing Techniques with a Reinforcement Learning Approach—0
Beyond PID Controllers: PPO with Neuralized PID Policy for Proton Beam Intensity Control in Mu2e—0
Resilient Constrained Reinforcement Learning—0
Rethinking Model-based, Policy-based, and Value-based Reinforcement Learning via the Lens of Representation ComplexityCode0
RL-MPCA: A Reinforcement Learning Based Multi-Phase Computation Allocation Approach for Recommender Systems—0
Conversational Question Answering with Reformulations over Knowledge Graph—0
General Method for Solving Four Types of SAT Problems—0
Learning Online Policies for Person Tracking in Multi-View Environments—0
A Bayesian Framework of Deep Reinforcement Learning for Joint O-RAN/MEC Orchestration—0
Agent based modelling for continuously varying supply chains—0
Hardware-Aware DNN Compression via Diverse Pruning and Mixed-Precision Quantization—0
Gradient Shaping for Multi-Constraint Safe Reinforcement Learning—0
Human-AI Collaboration in Real-World Complex Environment with Reinforcement Learning—0
Reinforcement Learning for Safe Occupancy Strategies in Educational Spaces during an Epidemic—0
Mutual Information as Intrinsic Reward of Reinforcement Learning Agents for On-demand Ride Pooling—0
Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning—0
REBEL: Reward Regularization-Based Approach for Robotic Reinforcement Learning from Human Feedback—0
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Benchmark Results

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