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 2851–2875 of 15113 papers

TitleStatusHype
SySLLM: Generating Synthesized Policy Summaries for Reinforcement Learning Agents Using Large Language Models—0
Representation-based Reward Modeling for Efficient Safety Alignment of Large Language Model—0
NIL: No-data Imitation Learning by Leveraging Pre-trained Video Diffusion Models—0
Safe Continual Domain Adaptation after Sim2Real Transfer of Reinforcement Learning Policies in Robotics—0
Scalable Evaluation of Online Facilitation Strategies via Synthetic Simulation of DiscussionsCode0
SortingEnv: An Extendable RL-Environment for an Industrial Sorting Process—0
Evaluating Reinforcement Learning Safety and Trustworthiness in Cyber-Physical Systems—0
Optimisation of the Accelerator Control by Reinforcement Learning: A Simulation-Based Approach—0
MarineGym: A High-Performance Reinforcement Learning Platform for Underwater Robotics—0
Solving Bayesian inverse problems with diffusion priors and off-policy RL—0
Unified Locomotion Transformer with Simultaneous Sim-to-Real Transfer for Quadrupeds—0
Local Look-Ahead Guidance via Verifier-in-the-Loop for Automated Theorem Proving—0
Edge AI-Powered Real-Time Decision-Making for Autonomous Vehicles in Adverse Weather Conditions—0
Large-scale Regional Traffic Signal Control Based on Single-Agent Reinforcement Learning—0
Balancing SoC in Battery Cells using Safe Action Perturbations—0
Disentangled World Models: Learning to Transfer Semantic Knowledge from Distracting Videos for Reinforcement Learning—0
Near-Optimal Sample Complexity for Iterated CVaR Reinforcement Learning with a Generative Model—0
MoRE: Unlocking Scalability in Reinforcement Learning for Quadruped Vision-Language-Action Models—0
HASARD: A Benchmark for Vision-Based Safe Reinforcement Learning in Embodied Agents—0
A Cascading Cooperative Multi-agent Framework for On-ramp Merging Control Integrating Large Language Models—0
In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents—0
Zero-Shot Action Generalization with Limited Observations—0
Adaptive routing protocols for determining optimal paths in AI multi-agent systems: a priority- and learning-enhanced approach—0
Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning—0
Efficient Neural Clause-Selection Reinforcement—0
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Benchmark Results

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