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 3351–3375 of 15113 papers

TitleStatusHype
Noise as a Double-Edged Sword: Reinforcement Learning Exploits Randomized Defenses in Neural Networks—0
Deterministic Exploration via Stationary Bellman Error Maximization—0
A Non-Monolithic Policy Approach of Offline-to-Online Reinforcement LearningCode0
Teaching Embodied Reinforcement Learning Agents: Informativeness and Diversity of Language UseCode0
Resource Governance in Networked Systems via Integrated Variational Autoencoders and Reinforcement Learning—0
Offline Behavior DistillationCode0
Self-Driving Car Racing: Application of Deep Reinforcement Learning—0
Offline Reinforcement Learning and Sequence Modeling for Downlink Link Adaptation—0
Return Augmented Decision Transformer for Off-Dynamics Reinforcement Learning—0
Stepping Out of the Shadows: Reinforcement Learning in Shadow Mode—0
SoftCTRL: Soft conservative KL-control of Transformer Reinforcement Learning for Autonomous Driving—0
Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning—0
A Multi-Agent Reinforcement Learning Testbed for Cognitive Radio Applications—0
FairStream: Fair Multimedia Streaming Benchmark for Reinforcement Learning AgentsCode0
The Limits of Transfer Reinforcement Learning with Latent Low-rank Structure—0
Getting By Goal Misgeneralization With a Little Help From a Mentor—0
Bilevel Model for Electricity Market Mechanism Optimisation via Quantum Computing Enhanced Reinforcement Learning—0
Off-Policy Selection for Initiating Human-Centric Experimental Design—0
GFlowNet Fine-tuning for Diverse Correct Solutions in Mathematical Reasoning Tasks—0
Beyond Simple Sum of Delayed Rewards: Non-Markovian Reward Modeling for Reinforcement Learning—0
Humanizing the Machine: Proxy Attacks to Mislead LLM Detectors—0
Reinforcement Learning for Aligning Large Language Models Agents with Interactive Environments: Quantifying and Mitigating Prompt Overfitting—0
AgentForge: A Flexible Low-Code Platform for Reinforcement Learning Agent DesignCode0
Random Policy Enables In-Context Reinforcement Learning within Trust Horizons—0
On-Robot Reinforcement Learning with Goal-Contrastive Rewards—0
Show:102550
← PrevPage 135 of 605Next →

Benchmark Results

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