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

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Showing 1501–1550 of 15113 papers

TitleStatusHype
A Reinforcement Learning Engine with Reduced Action and State Space for Scalable Cyber-Physical Optimal Response—0
Improved Off-policy Reinforcement Learning in Biological Sequence DesignCode0
DeepLTL: Learning to Efficiently Satisfy Complex LTL Specifications for Multi-Task RL—0
Improving Portfolio Optimization Results with Bandit NetworksCode0
Spatial-aware decision-making with ring attractors in reinforcement learning systems—0
Predictive Coding for Decision TransformerCode1
Mitigating Adversarial Perturbations for Deep Reinforcement Learning via Vector QuantizationCode1
CLoSD: Closing the Loop between Simulation and Diffusion for multi-task character controlCode3
Solving Reach-Avoid-Stay Problems Using Deep Deterministic Policy Gradients—0
Efficient Residual Learning with Mixture-of-Experts for Universal Dexterous Grasping—0
ReLIC: A Recipe for 64k Steps of In-Context Reinforcement Learning for Embodied AICode1
Learning Emergence of Interaction Patterns across Independent RL Agents in Multi-Agent Environments—0
Cross-Embodiment Dexterous Grasping with Reinforcement Learning—0
End-to-end Driving in High-Interaction Traffic Scenarios with Reinforcement Learning—0
Dual Active Learning for Reinforcement Learning from Human Feedback—0
Beyond Expected Returns: A Policy Gradient Algorithm for Cumulative Prospect Theoretic Reinforcement Learning—0
The Smart Buildings Control Suite: A Diverse Open Source Benchmark to Evaluate and Scale HVAC Control Policies for Sustainability—0
ComaDICE: Offline Cooperative Multi-Agent Reinforcement Learning with Stationary Distribution Shift Regularization—0
Don't flatten, tokenize! Unlocking the key to SoftMoE's efficacy in deep RL—0
LLM-Augmented Symbolic Reinforcement Learning with Landmark-Based Task Decomposition—0
Adaptive teachers for amortized samplersCode0
Sampling from Energy-based Policies using Diffusion—0
VinePPO: Unlocking RL Potential For LLM Reasoning Through Refined Credit AssignmentCode2
Sparse Autoencoders Reveal Temporal Difference Learning in Large Language Models—0
Bellman Diffusion: Generative Modeling as Learning a Linear Operator in the Distribution Space—0
Scalable Reinforcement Learning-based Neural Architecture Search—0
PreND: Enhancing Intrinsic Motivation in Reinforcement Learning through Pre-trained Network Distillation—0
Absolute State-wise Constrained Policy Optimization: High-Probability State-wise Constraints Satisfaction—0
Scaling Offline Model-Based RL via Jointly-Optimized World-Action Model PretrainingCode1
Upper and Lower Bounds for Distributionally Robust Off-Dynamics Reinforcement Learning—0
Task-agnostic Pre-training and Task-guided Fine-tuning for Versatile Diffusion Planner—0
Personalisation via Dynamic Policy Fusion—0
Focus On What Matters: Separated Models For Visual-Based RL Generalization—0
Analysis on Riemann Hypothesis with Cross Entropy Optimization and Reasoning—0
Constrained Reinforcement Learning for Safe Heat Pump ControlCode0
Grounded Curriculum Learning—0
Generalizing Consistency Policy to Visual RL with Prioritized Proximal Experience Regularization—0
Learning to Bridge the Gap: Efficient Novelty Recovery with Planning and Reinforcement Learning—0
Strongly-polynomial time and validation analysis of policy gradient methods—0
Climate Adaptation with Reinforcement Learning: Experiments with Flooding and Transportation in CopenhagenCode0
ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement LearningCode1
Enhancing Spectrum Efficiency in 6G Satellite Networks: A GAIL-Powered Policy Learning via Asynchronous Federated Inverse Reinforcement Learning—0
TemporalPaD: a reinforcement-learning framework for temporal feature representation and dimension reduction—0
CurricuLLM: Automatic Task Curricula Design for Learning Complex Robot Skills using Large Language ModelsCode1
Cost-Aware Dynamic Cloud Workflow Scheduling using Self-Attention and Evolutionary Reinforcement Learning—0
Optimizing Downlink C-NOMA Transmission with Movable Antennas: A DDPG-based Approach—0
DMC-VB: A Benchmark for Representation Learning for Control with Visual DistractorsCode1
LoopSR: Looping Sim-and-Real for Lifelong Policy Adaptation of Legged Robots—0
Autoregressive Multi-trait Essay Scoring via Reinforcement Learning with Scoring-aware Multiple Rewards—0
Asynchronous Fractional Multi-Agent Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing—0
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Benchmark Results

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