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 3001–3025 of 15113 papers

TitleStatusHype
Behavior-Regularized Diffusion Policy Optimization for Offline Reinforcement Learning—0
Enhancing Pre-Trained Decision Transformers with Prompt-Tuning Bandits—0
Towards Smarter Sensing: 2D Clutter Mitigation in RL-Driven Cognitive MIMO Radar—0
Convergent NMPC-based Reinforcement Learning Using Deep Expected Sarsa and Nonlinear Temporal Difference Learning—0
Adversarially-Robust TD Learning with Markovian Data: Finite-Time Rates and Fundamental Limits—0
Learning Strategic Language Agents in the Werewolf Game with Iterative Latent Space Policy Optimization—0
Behavioral Entropy-Guided Dataset Generation for Offline Reinforcement Learning—0
Autotelic Reinforcement Learning: Exploring Intrinsic Motivations for Skill Acquisition in Open-Ended Environments—0
Reinforcement Learning Based Prediction of PID Controller Gains for Quadrotor UAVs—0
Illuminating Spaces: Deep Reinforcement Learning and Laser-Wall Partitioning for Architectural Layout Generation—0
Mirror Descent Actor Critic via Bounded Advantage Learning—0
LLM Alignment as Retriever Optimization: An Information Retrieval Perspective—0
Transforming Multimodal Models into Action Models for Radiotherapy—0
Optimizing Electric Vehicles Charging using Large Language Models and Graph Neural Networks—0
AI-driven materials design: a mini-review—0
OmniRL: In-Context Reinforcement Learning by Large-Scale Meta-Training in Randomized Worlds—0
Calibrated Unsupervised Anomaly Detection in Multivariate Time-series using Reinforcement Learning—0
Underwater Soft Fin Flapping Motion with Deep Neural Network Based Surrogate ModelCode0
RAPID: Robust and Agile Planner Using Inverse Reinforcement Learning for Vision-Based Drone Navigation—0
Adviser-Actor-Critic: Eliminating Steady-State Error in Reinforcement Learning Control—0
VolleyBots: A Testbed for Multi-Drone Volleyball Game Combining Motion Control and Strategic Play—0
Circular Microalgae-Based Carbon Control for Net ZeroCode0
Brief analysis of DeepSeek R1 and it's implications for Generative AI—0
Preference VLM: Leveraging VLMs for Scalable Preference-Based Reinforcement Learning—0
Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning—0
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Benchmark Results

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