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 4801–4825 of 15113 papers

TitleStatusHype
Reinforcement Learning for Generative AI: State of the Art, Opportunities and Open Research Challenges—0
Reinforcement Learning Under Probabilistic Spatio-Temporal Constraints with Time Windows—0
Using Implicit Behavior Cloning and Dynamic Movement Primitive to Facilitate Reinforcement Learning for Robot Motion Planning—0
PIMbot: Policy and Incentive Manipulation for Multi-Robot Reinforcement Learning in Social DilemmasCode0
Shrink-Perturb Improves Architecture Mixing during Population Based Training for Neural Architecture SearchCode0
Primitive Skill-based Robot Learning from Human Evaluative Feedback—0
TrackAgent: 6D Object Tracking via Reinforcement Learning—0
Dialogue Shaping: Empowering Agents through NPC Interaction—0
ETHER: Aligning Emergent Communication for Hindsight Experience Replay—0
Approximate Model-Based Shielding for Safe Reinforcement LearningCode0
Controlling the Latent Space of GANs through Reinforcement Learning: A Case Study on Task-based Image-to-Image Translation—0
Actions Speak What You Want: Provably Sample-Efficient Reinforcement Learning of the Quantal Stackelberg Equilibrium from Strategic Feedbacks—0
Reinforcement Learning by Guided Safe Exploration—0
Mode-constrained Model-based Reinforcement Learning via Gaussian ProcessesCode0
Unbiased Weight Maximization—0
Structural Credit Assignment with Coordinated Exploration—0
The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation—0
Reinforcement Learning -based Adaptation and Scheduling Methods for Multi-source DASHCode0
Offline Reinforcement Learning with On-Policy Q-Function Regularization—0
Settling the Sample Complexity of Online Reinforcement Learning—0
Counterfactual Explanation Policies in RL—0
Communication-Efficient Orchestrations for URLLC Service via Hierarchical Reinforcement Learning—0
ExWarp: Extrapolation and Warping-based Temporal Supersampling for High-frequency Displays—0
Theoretically Guaranteed Policy Improvement Distilled from Model-Based Planning—0
On the Effectiveness of Offline RL for Dialogue Response GenerationCode0
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Benchmark Results

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