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 6201–6225 of 15113 papers

TitleStatusHype
Learn Quasi-stationary Distributions of Finite State Markov Chain—0
Learn to Earn: Enabling Coordination within a Ride Hailing Fleet—0
Learn to Exceed: Stereo Inverse Reinforcement Learning with Concurrent Policy Optimization—0
Learn To Manage Portfolio With Reinforcement Learning—0
Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets—0
Learn to Play Tetris with Deep Reinforcement Learning—0
Learn to Teach: Sample-Efficient Privileged Learning for Humanoid Locomotion over Diverse Terrains—0
Learn to Tour: Operator Design For Solution Feasibility Mapping in Pickup-and-delivery Traveling Salesman Problem—0
Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning—0
Learn Zero-Constraint-Violation Policy in Model-Free Constrained Reinforcement Learning—0
Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator—0
On the Model-Misspecification in Reinforcement Learning—0
Lecture Notes on Partially Known MDPs—0
LeDeepChef: Deep Reinforcement Learning Agent for Families of Text-Based Games—0
Score vs. Winrate in Score-Based Games: which Reward for Reinforcement Learning?—0
Lessons from reinforcement learning for biological representations of space—0
Lessons Learned from Data-Driven Building Control Experiments: Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement Learning—0
Less Suboptimal Learning and Control in Variational POMDPs—0
Let's Reason Formally: Natural-Formal Hybrid Reasoning Enhances LLM's Math Capability—0
Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking—0
Cognitive Level-k Meta-Learning for Safe and Pedestrian-Aware Autonomous Driving—0
Leverage the Average: an Analysis of KL Regularization in RL—0
Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning—0
Leveraging Domain-Unlabeled Data in Offline Reinforcement Learning across Two Domains—0
Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis—0
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Benchmark Results

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