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 2851–2875 of 15113 papers

TitleStatusHype
Contrastive Preference Learning: Learning from Human Feedback without RLCode1
Towards Robust Offline Reinforcement Learning under Diverse Data CorruptionCode1
Vision-Language Models are Zero-Shot Reward Models for Reinforcement LearningCode1
SDGym: Low-Code Reinforcement Learning Environments using System Dynamics Models—0
Learning to Optimise Climate Sensor Placement using a Transformer—0
On The Expressivity of Objective-Specification Formalisms in Reinforcement Learning—0
Accelerate Presolve in Large-Scale Linear Programming via Reinforcement Learning—0
Accelerated Policy Gradient: On the Convergence Rates of the Nesterov Momentum for Reinforcement LearningCode0
Improving Generalization of Alignment with Human Preferences through Group Invariant Learning—0
Using Experience Classification for Training Non-Markovian Tasks—0
Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven OptimizationCode1
Action-Quantized Offline Reinforcement Learning for Robotic Skill Learning—0
Neural Packing: from Visual Sensing to Reinforcement Learning—0
Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning—0
Reinforcement learning with non-ergodic reward increments: robustness via ergodicity transformationsCode0
Uncertainty-aware transfer across tasks using hybrid model-based successor feature reinforcement learning—0
Building Persona Consistent Dialogue Agents with Offline Reinforcement LearningCode0
Leveraging Topological Maps in Deep Reinforcement Learning for Multi-Object Navigation—0
AMAGO: Scalable In-Context Reinforcement Learning for Adaptive AgentsCode1
Deep Reinforcement Learning with Explicit Context Representation—0
A Framework for Empowering Reinforcement Learning Agents with Causal Analysis: Enhancing Automated Cryptocurrency Trading—0
LgTS: Dynamic Task Sampling using LLM-generated sub-goals for Reinforcement Learning Agents—0
Reduced Policy Optimization for Continuous Control with Hard ConstraintsCode1
Hybrid Reinforcement Learning for Optimizing Pump Sustainability in Real-World Water Distribution Networks—0
Exploration with Principles for Diverse AI Supervision—0
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Benchmark Results

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