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 8776–8800 of 15113 papers

TitleStatusHype
RAPID-RL: A Reconfigurable Architecture with Preemptive-Exits for Efficient Deep-Reinforcement Learning—0
RAPID: Robust and Agile Planner Using Inverse Reinforcement Learning for Vision-Based Drone Navigation—0
RAP: Runtime-Adaptive Pruning for LLM Inference—0
RASR: Risk-Averse Soft-Robust MDPs with EVaR and Entropic Risk—0
RaSS: Improving Denoising Diffusion Samplers with Reinforced Active Sampling Scheduler—0
Rate-matching the regret lower-bound in the linear quadratic regulator with unknown dynamics—0
Rating Continuous Actions in Spatial Multi-Agent Problems—0
Innate-Values-driven Reinforcement Learning based Cognitive Modeling—0
Raw2Drive: Reinforcement Learning with Aligned World Models for End-to-End Autonomous Driving (in CARLA v2)—0
Ray Interference: a Source of Plateaus in Deep Reinforcement Learning—0
RBED: Reward Based Epsilon Decay—0
RbRL2.0: Integrated Reward and Policy Learning for Rating-based Reinforcement Learning—0
Triples-to-Text Generation with Reinforcement Learning Based Graph-augmented Neural Networks—0
Reachability analysis in stochastic directed graphs by reinforcement learning—0
Reachability-Aware Laplacian Representation in Reinforcement Learning—0
Reachability Traces for Curriculum Design in Reinforcement Learning—0
Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning—0
Reactive Reinforcement Learning in Asynchronous Environments—0
REACT: Revealing Evolutionary Action Consequence Trajectories for Interpretable Reinforcement Learning—0
Real2Sim or Sim2Real: Robotics Visual Insertion using Deep Reinforcement Learning and Real2Sim Policy Adaptation—0
REALab: An Embedded Perspective on Tampering—0
Real Robot Challenge 2022: Learning Dexterous Manipulation from Offline Data in the Real World—0
Real-time Active Vision for a Humanoid Soccer Robot Using Deep Reinforcement Learning—0
Offline to Online Learning for Real-Time Bandwidth Estimation—0
Real-Time Bayesian Detection of Drift-Evasive GNSS Spoofing in Reinforcement Learning Based UAV Deconfliction—0
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Benchmark Results

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