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 2576–2600 of 15113 papers

TitleStatusHype
Unveiling the Black Box: A Multi-Layer Framework for Explaining Reinforcement Learning-Based Cyber Agents—0
Is PRM Necessary? Problem-Solving RL Implicitly Induces PRM Capability in LLMs—0
Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes—0
ShiQ: Bringing back Bellman to LLMs—0
Certifying Stability of Reinforcement Learning Policies using Generalized Lyapunov Functions—0
Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLMCode0
Learning When to Think: Shaping Adaptive Reasoning in R1-Style Models via Multi-Stage RLCode0
Developing and Integrating Trust Modeling into Multi-Objective Reinforcement Learning for Intelligent Agricultural Management—0
Attention-Based Reward Shaping for Sparse and Delayed RewardsCode0
Reinforcement Learning for AMR Charging Decisions: The Impact of Reward and Action Space Design—0
Spectral Policy Optimization: Coloring your Incorrect Reasoning in GRPO—0
Time-R1: Towards Comprehensive Temporal Reasoning in LLMsCode0
Reinforcement Learning Finetunes Small Subnetworks in Large Language Models—0
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change—0
Fine-tuning Diffusion Policies with Backpropagation Through Diffusion Timesteps—0
IN-RIL: Interleaved Reinforcement and Imitation Learning for Policy Fine-TuningCode0
Reinforcing the Diffusion Chain of Lateral Thought with Diffusion Language Models—0
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation—0
TensorRL-QAS: Reinforcement learning with tensor networks for scalable quantum architecture search—0
Reinforcement Learning for Individual Optimal Policy from Heterogeneous Data—0
Risk-Aware Safe Reinforcement Learning for Control of Stochastic Linear Systems—0
CEC-Zero: Chinese Error Correction Solution Based on LLM—0
Adaptive Security Policy Management in Cloud Environments Using Reinforcement Learning—0
Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning—0
Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles—0
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Benchmark Results

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