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 451–475 of 15113 papers

TitleStatusHype
Automatic Curriculum Learning for Driving Scenarios: Towards Robust and Efficient Reinforcement Learning—0
Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning—0
Preference Optimization for Combinatorial Optimization Problems—0
The Exploratory Multi-Asset Mean-Variance Portfolio Selection using Reinforcement Learning—0
DARLR: Dual-Agent Offline Reinforcement Learning for Recommender Systems with Dynamic RewardCode0
Combining Bayesian Inference and Reinforcement Learning for Agent Decision Making: A Review—0
Measuring General Intelligence with Generated GamesCode1
INTELLECT-2: A Reasoning Model Trained Through Globally Decentralized Reinforcement Learning—0
Cache-Efficient Posterior Sampling for Reinforcement Learning with LLM-Derived Priors Across Discrete and Continuous Domains—0
Kalman Filter Enhanced GRPO for Reinforcement Learning-Based Language Model ReasoningCode1
Agent RL Scaling Law: Agent RL with Spontaneous Code Execution for Mathematical Problem SolvingCode2
Reinforced Internal-External Knowledge Synergistic Reasoning for Efficient Adaptive Search AgentCode2
Selftok: Discrete Visual Tokens of Autoregression, by Diffusion, and for Reasoning—0
DanceGRPO: Unleashing GRPO on Visual GenerationCode5
DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented GenerationCode2
Design and Experimental Test of Datatic Approximate Optimal Filter in Nonlinear Dynamic Systems—0
Learning Value of Information towards Joint Communication and Control in 6G V2X—0
Reinforcement Learning (RL) Meets Urban Climate Modeling: Investigating the Efficacy and Impacts of RL-Based HVAC Control—0
FACET: Force-Adaptive Control via Impedance Reference Tracking for Legged Robots—0
X-Sim: Cross-Embodiment Learning via Real-to-Sim-to-Real—0
REFINE-AF: A Task-Agnostic Framework to Align Language Models via Self-Generated Instructions using Reinforcement Learning from Automated Feedback—0
LineFlow: A Framework to Learn Active Control of Production LinesCode0
Balancing Progress and Safety: A Novel Risk-Aware Objective for RL in Autonomous Driving—0
Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach—0
Interaction-Aware Parameter Privacy-Preserving Data Sharing in Coupled Systems via Particle Filter Reinforcement Learning—0
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Benchmark Results

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