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 1501–1525 of 15113 papers

TitleStatusHype
AdaMemento: Adaptive Memory-Assisted Policy Optimization for Reinforcement Learning—0
A Reinforcement Learning Engine with Reduced Action and State Space for Scalable Cyber-Physical Optimal Response—0
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
CLoSD: Closing the Loop between Simulation and Diffusion for multi-task character controlCode3
Mitigating Adversarial Perturbations for Deep Reinforcement Learning via Vector QuantizationCode1
Solving Reach-Avoid-Stay Problems Using Deep Deterministic Policy Gradients—0
ReLIC: A Recipe for 64k Steps of In-Context Reinforcement Learning for Embodied AICode1
Cross-Embodiment Dexterous Grasping with Reinforcement Learning—0
Learning Emergence of Interaction Patterns across Independent RL Agents in Multi-Agent Environments—0
End-to-end Driving in High-Interaction Traffic Scenarios with Reinforcement Learning—0
Dual Active Learning for Reinforcement Learning from Human Feedback—0
Efficient Residual Learning with Mixture-of-Experts for Universal Dexterous Grasping—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
LLM-Augmented Symbolic Reinforcement Learning with Landmark-Based Task Decomposition—0
Don't flatten, tokenize! Unlocking the key to SoftMoE's efficacy in deep RL—0
ComaDICE: Offline Cooperative Multi-Agent Reinforcement Learning with Stationary Distribution Shift Regularization—0
Adaptive teachers for amortized samplersCode0
Sparse Autoencoders Reveal Temporal Difference Learning in Large Language Models—0
PreND: Enhancing Intrinsic Motivation in Reinforcement Learning through Pre-trained Network Distillation—0
Scalable Reinforcement Learning-based Neural Architecture Search—0
VinePPO: Unlocking RL Potential For LLM Reasoning Through Refined Credit AssignmentCode2
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Benchmark Results

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