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 3476–3500 of 15113 papers

TitleStatusHype
Improved Off-policy Reinforcement Learning in Biological Sequence DesignCode0
A Reinforcement Learning Engine with Reduced Action and State Space for Scalable Cyber-Physical Optimal Response—0
Improving Portfolio Optimization Results with Bandit NetworksCode0
Spatial-aware decision-making with ring attractors in reinforcement learning systems—0
Solving Reach-Avoid-Stay Problems Using Deep Deterministic Policy Gradients—0
Cross-Embodiment Dexterous Grasping with Reinforcement Learning—0
Learning Emergence of Interaction Patterns across Independent RL Agents in Multi-Agent Environments—0
Efficient Residual Learning with Mixture-of-Experts for Universal Dexterous Grasping—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
Adaptive teachers for amortized samplersCode0
The Smart Buildings Control Suite: A Diverse Open Source Benchmark to Evaluate and Scale HVAC Control Policies for Sustainability—0
Scalable Reinforcement Learning-based Neural Architecture Search—0
Sparse Autoencoders Reveal Temporal Difference Learning in Large Language Models—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
ComaDICE: Offline Cooperative Multi-Agent Reinforcement Learning with Stationary Distribution Shift Regularization—0
Sampling from Energy-based Policies using Diffusion—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
Bellman Diffusion: Generative Modeling as Learning a Linear Operator in the Distribution Space—0
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
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Benchmark Results

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