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 4251–4275 of 15113 papers

TitleStatusHype
Monitored Markov Decision ProcessesCode0
Value function interference and greedy action selection in value-based multi-objective reinforcement learning—0
ACTER: Diverse and Actionable Counterfactual Sequences for Explaining and Diagnosing RL Policies—0
High-Precision Geosteering via Reinforcement Learning and Particle Filters—0
Learn to Teach: Sample-Efficient Privileged Learning for Humanoid Locomotion over Diverse Terrains—0
Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices—0
Differentially Private Deep Model-Based Reinforcement Learning—0
Multi-Timescale Ensemble Q-learning for Markov Decision Process Policy OptimizationCode0
Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RLCode0
Scaling Intelligent Agents in Combat Simulations for Wargaming—0
Real-World Fluid Directed Rigid Body Control via Deep Reinforcement Learning—0
OIL-AD: An Anomaly Detection Framework for Sequential Decision SequencesCode0
Convergence for Natural Policy Gradient on Infinite-State Queueing MDPs—0
Code as Reward: Empowering Reinforcement Learning with VLMs—0
Learning Diverse Policies with Soft Self-Generated Guidance—0
Learning by Doing: An Online Causal Reinforcement Learning Framework with Causal-Aware Policy—0
Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits—0
A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear MDPs—0
Averaging n-step Returns Reduces Variance in Reinforcement Learning—0
Logical Specifications-guided Dynamic Task Sampling for Reinforcement Learning AgentsCode0
No-Regret Reinforcement Learning in Smooth MDPs—0
Reinforcement Learning from Bagged Reward—0
Understanding What Affects the Generalization Gap in Visual Reinforcement Learning: Theory and Empirical Evidence—0
Replication of Impedance Identification Experiments on a Reinforcement-Learning-Controlled Digital Twin of Human ElbowsCode0
Vision-Language Models Provide Promptable Representations for Reinforcement Learning—0
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Benchmark Results

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