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 3201–3250 of 15113 papers

TitleStatusHype
Prioritized Trajectory Replay: A Replay Memory for Data-driven Reinforcement Learning—0
Learning to Modulate pre-trained Models in RLCode1
InterCode: Standardizing and Benchmarking Interactive Coding with Execution FeedbackCode2
Augmenting Control over Exploration Space in Molecular Dynamics Simulators to Streamline De Novo Analysis through Generative Control Policies—0
Estimating player completion rate in mobile puzzle games using reinforcement learning—0
Multivariate Time Series Early Classification Across Channel and Time DimensionsCode0
Supervised Pretraining Can Learn In-Context Reinforcement Learning—0
ChiPFormer: Transferable Chip Placement via Offline Decision Transformer—0
Decentralized Multi-Robot Formation Control Using Reinforcement Learning—0
PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks—0
A Framework for dynamically meeting performance objectives on a service mesh—0
Is RLHF More Difficult than Standard RL?—0
Fighting Uncertainty with Gradients: Offline Reinforcement Learning via Diffusion Score Matching—0
Towards Optimal Pricing of Demand Response -- A Nonparametric Constrained Policy Optimization Approach—0
Reinforcement Learning with Temporal-Logic-Based Causal Diagrams—0
Active Coverage for PAC Reinforcement Learning—0
CLUE: Calibrated Latent Guidance for Offline Reinforcement Learning—0
MP3: Movement Primitive-Based (Re-)Planning Policy—0
TACO: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement LearningCode1
Transferable Curricula through Difficulty Conditioned Generators—0
Harnessing Mixed Offline Reinforcement Learning Datasets via Trajectory WeightingCode1
Beyond OOD State Actions: Supported Cross-Domain Offline Reinforcement LearningCode1
State-wise Constrained Policy OptimizationCode1
AdCraft: An Advanced Reinforcement Learning Benchmark Environment for Search Engine Marketing OptimizationCode0
Learning to Generate Better Than Your LLMCode1
Efficient Dynamics Modeling in Interactive Environments with Koopman Theory—0
Int-HRL: Towards Intention-based Hierarchical Reinforcement Learning—0
Reward Shaping via Diffusion Process in Reinforcement Learning—0
Autonomous Driving with Deep Reinforcement Learning in CARLA Simulation—0
Neural Inventory Control in Networks via Hindsight Differentiable Policy OptimizationCode1
Warm-Start Actor-Critic: From Approximation Error to Sub-optimality Gap—0
Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable EnvironmentCode1
Adaptive Ordered Information Extraction with Deep Reinforcement LearningCode0
PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement LearningCode1
AdaStop: adaptive statistical testing for sound comparisons of Deep RL agentsCode0
On the Model-Misspecification in Reinforcement Learning—0
Enhancing variational quantum state diagonalization using reinforcement learning techniquesCode0
Acceleration in Policy Optimization—0
The RL Perceptron: Generalisation Dynamics of Policy Learning in High Dimensions—0
Active Policy Improvement from Multiple Black-box OraclesCode0
Genes in Intelligent AgentsCode0
Do as I can, not as I get—0
The False Dawn: Reevaluating Google's Reinforcement Learning for Chip Macro Placement—0
Bootstrapped Representations in Reinforcement Learning—0
Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAXCode2
Semi-Offline Reinforcement Learning for Optimized Text GenerationCode0
Temporal Difference Learning with Experience Replay—0
Low-Switching Policy Gradient with Exploration via Online Sensitivity Sampling—0
Offline Multi-Agent Reinforcement Learning with Coupled Value Factorization—0
Granger Causal Interaction Skill Chains—0
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Benchmark Results

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