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 6251–6300 of 15113 papers

TitleStatusHype
Limited Query Graph Connectivity Test—0
Limits of Actor-Critic Algorithms for Decision Tree Policies Learning in IBMDPs—0
Lineage Evolution Reinforcement Learning—0
Linear Bellman Completeness Suffices for Efficient Online Reinforcement Learning with Few Actions—0
Linear Complementarity for Regularized Policy Evaluation and Improvement—0
Linear convergence of a policy gradient method for some finite horizon continuous time control problems—0
Linear Feature Encoding for Reinforcement Learning—0
Linear interpolation gives better gradients than Gaussian smoothing in derivative-free optimization—0
Linear-Quadratic Mean-Field Reinforcement Learning: Convergence of Policy Gradient Methods—0
Logarithmic regret for episodic continuous-time linear-quadratic reinforcement learning over a finite-time horizon—0
Linear Reinforcement Learning with Ball Structure Action Space—0
Linear Representation Meta-Reinforcement Learning for Instant Adaptation—0
Linear Stochastic Approximation: Constant Step-Size and Iterate Averaging—0
LISPR: An Options Framework for Policy Reuse with Reinforcement Learning—0
Listener-Rewarded Thinking in VLMs for Image Preferences—0
LlamaRL: A Distributed Asynchronous Reinforcement Learning Framework for Efficient Large-scale LLM Trainin—0
LLM Alignment as Retriever Optimization: An Information Retrieval Perspective—0
LLM Augmented Hierarchical Agents—0
LLM-Augmented Symbolic Reinforcement Learning with Landmark-Based Task Decomposition—0
LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions—0
LLM-Based Offline Learning for Embodied Agents via Consistency-Guided Reward Ensemble—0
LLM-Explorer: A Plug-in Reinforcement Learning Policy Exploration Enhancement Driven by Large Language Models—0
LLM-hRIC: LLM-empowered Hierarchical RAN Intelligent Control for O-RAN—0
LLMs for Engineering: Teaching Models to Design High Powered Rockets—0
LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard—0
LLMStinger: Jailbreaking LLMs using RL fine-tuned LLMs—0
LLQL: Logistic Likelihood Q-Learning for Reinforcement Learning—0
Reward Guidance for Reinforcement Learning Tasks Based on Large Language Models: The LMGT Framework—0
Local Advantage Actor-Critic for Robust Multi-Agent Deep Reinforcement Learning—0
Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning—0
Local Communication Protocols for Learning Complex Swarm Behaviors with Deep Reinforcement Learning—0
Local Differential Privacy for Regret Minimization in Reinforcement Learning—0
Local Environment Poisoning Attacks on Federated Reinforcement Learning—0
LocalEscaper: A Weakly-supervised Framework with Regional Reconstruction for Scalable Neural TSP Solvers—0
Local Explanations for Reinforcement Learning—0
Local Feature Swapping for Generalization in Reinforcement Learning—0
Local-Guided Global: Paired Similarity Representation for Visual Reinforcement Learning—0
Locality Matters: A Scalable Value Decomposition Approach for Cooperative Multi-Agent Reinforcement Learning—0
Localized Observation Abstraction Using Piecewise Linear Spatial Decay for Reinforcement Learning in Combat Simulations—0
Localizing by Describing: Attribute-Guided Attention Localization for Fine-Grained Recognition—0
Local Linearity: the Key for No-regret Reinforcement Learning in Continuous MDPs—0
Local Look-Ahead Guidance via Verifier-in-the-Loop for Automated Theorem Proving—0
Locally Constrained Representations in Reinforcement Learning—0
Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes—0
Locally Private Distributed Reinforcement Learning—0
Local Navigation and Docking of an Autonomous Robot Mower using Reinforcement Learning and Computer Vision—0
Local Nonstationarity for Efficient Bayesian Optimization—0
Local Pairwise Distance Matching for Backpropagation-Free Reinforcement Learning—0
Local Policy Optimization for Trajectory-Centric Reinforcement Learning—0
Local Search for Policy Iteration in Continuous Control—0
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Benchmark Results

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