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 3226–3250 of 15113 papers

TitleStatusHype
Out-of-Distribution Detection for Neurosymbolic Autonomous Cyber Agents—0
AI-Driven Resource Allocation Framework for Microservices in Hybrid Cloud Platforms—0
Technical Report on Reinforcement Learning Control on the Lucas-Nülle Inverted Pendulum—0
Generating Critical Scenarios for Testing Automated Driving Systems—0
Reinforcement learning to learn quantum states for Heisenberg scaling accuracyCode0
Selective Reviews of Bandit Problems in AI via a Statistical View—0
A Memory-Based Reinforcement Learning Approach to Integrated Sensing and Communication—0
Dense Dynamics-Aware Reward Synthesis: Integrating Prior Experience with Demonstrations—0
Explore Reinforced: Equilibrium Approximation with Reinforcement Learning—0
Approximately Optimal Search on a Higher-dimensional Sliding PuzzleCode0
RL2: Reinforce Large Language Model to Assist Safe Reinforcement Learning for Energy Management of Active Distribution Networks—0
Provable Partially Observable Reinforcement Learning with Privileged Information—0
Bilinear Convolution Decomposition for Causal RL Interpretability—0
BOTS: Batch Bayesian Optimization of Extended Thompson Sampling for Severely Episode-Limited RL Settings—0
HVAC-DPT: A Decision Pretrained Transformer for HVAC Control—0
Solving Rubik's Cube Without Tricky Sampling—0
RL-MILP Solver: A Reinforcement Learning Approach for Solving Mixed-Integer Linear Programs with Graph Neural Networks—0
TEA: Trajectory Encoding Augmentation for Robust and Transferable Policies in Offline Reinforcement Learning—0
Convex Regularization and Convergence of Policy Gradient Flows under Safety Constraints—0
A Comprehensive Survey of Reinforcement Learning: From Algorithms to Practical Challenges—0
Supervised Learning-enhanced Multi-Group Actor Critic for Live Stream Allocation in FeedCode0
NeoHebbian Synapses to Accelerate Online Training of Neuromorphic Hardware—0
Dynamic Non-Prehensile Object Transport via Model-Predictive Reinforcement Learning—0
ScaleViz: Scaling Visualization Recommendation Models on Large Data—0
ELEMENTAL: Interactive Learning from Demonstrations and Vision-Language Models for Reward Design in Robotics—0
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Benchmark Results

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