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 4176–4200 of 15113 papers

TitleStatusHype
Simulating Battery-Powered TinyML Systems Optimised using Reinforcement Learning in Image-Based Anomaly Detection—0
Why Online Reinforcement Learning is Causal—0
Zero-shot cross-modal transfer of Reinforcement Learning policies through a Global WorkspaceCode0
Proxy-RLHF: Decoupling Generation and Alignment in Large Language Model with Proxy—0
Noisy Spiking Actor Network for Exploration—0
RL-CFR: Improving Action Abstraction for Imperfect Information Extensive-Form Games with Reinforcement Learning—0
Learning Human-to-Humanoid Real-Time Whole-Body Teleoperation—0
A Natural Extension To Online Algorithms For Hybrid RL With Limited Coverage—0
Dexterous Legged Locomotion in Confined 3D Spaces with Reinforcement Learning—0
Belief-Enriched Pessimistic Q-Learning against Adversarial State PerturbationsCode0
Stop Regressing: Training Value Functions via Classification for Scalable Deep RL—0
Language Guided Exploration for RL Agents in Text Environments—0
Koopman-Assisted Reinforcement Learning—0
Iterated Q-Network: Beyond One-Step Bellman Updates in Deep Reinforcement Learning—0
Twisting Lids Off with Two Hands—0
Sample Efficient Myopic Exploration Through Multitask Reinforcement Learning with Diverse Tasks—0
Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey—0
Overestimation, Overfitting, and Plasticity in Actor-Critic: the Bitter Lesson of Reinforcement Learning—0
Conflict-Averse Gradient Aggregation for Constrained Multi-Objective Reinforcement Learning—0
Robustifying a Policy in Multi-Agent RL with Diverse Cooperative Behaviors and Adversarial Style Sampling for Assistive Tasks—0
Robust Policy Learning via Offline Skill Diffusion—0
RL-GPT: Integrating Reinforcement Learning and Code-as-policy—0
Offline Fictitious Self-Play for Competitive Games—0
Investigating Gender Fairness in Machine Learning-driven Personalized Care for Chronic Pain—0
Reinforcement Learning and Graph Neural Networks for Probabilistic Risk Assessment—0
Show:102550
← PrevPage 168 of 605Next →

Benchmark Results

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