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 3651–3675 of 15113 papers

TitleStatusHype
Intelligent O-RAN Traffic Steering for URLLC Through Deep Reinforcement Learning—0
Hindsight States: Blending Sim and Real Task Elements for Efficient Reinforcement Learning—0
CoRL: Environment Creation and Management Focused on System IntegrationCode1
RePreM: Representation Pre-training with Masked Model for Reinforcement Learning—0
Learning to Influence Human Behavior with Offline Reinforcement Learning—0
Toward Risk-based Optimistic Exploration for Cooperative Multi-Agent Reinforcement Learning—0
POPGym: Benchmarking Partially Observable Reinforcement LearningCode2
Guarded Policy Optimization with Imperfect Online Demonstrations—0
Approximating Energy Market Clearing and Bidding With Model-Based Reinforcement Learning—0
Tile Networks: Learning Optimal Geometric Layout for Whole-page Recommendation—0
T-Cell Receptor Optimization with Reinforcement Learning and Mutation Policies for Precesion Immunotherapy—0
Data-efficient, Explainable and Safe Box Manipulation: Illustrating the Advantages of Physical Priors in Model-Predictive Control—0
Multi-Start Team Orienteering Problem for UAS Mission Re-Planning with Data-Efficient Deep Reinforcement Learning—0
Reinforced Labels: Multi-Agent Deep Reinforcement Learning for Point-Feature Label Placement—0
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning—0
The Ladder in Chaos: A Simple and Effective Improvement to General DRL Algorithms by Policy Path Trimming and Boosting—0
GHQ: Grouped Hybrid Q Learning for Heterogeneous Cooperative Multi-agent Reinforcement LearningCode0
Domain Adaptation of Reinforcement Learning Agents based on Network Service Proximity—0
Reinforcement Learning Guided Multi-Objective Exam Paper GenerationCode0
Preference Transformer: Modeling Human Preferences using Transformers for RLCode1
Compensating for Sensing Failures via Delegation in Human-AI Hybrid Systems—0
Co-learning Planning and Control Policies Constrained by Differentiable Logic Specifications—0
Self-Improving Robots: End-to-End Autonomous Visuomotor Reinforcement Learning—0
Parameter Sharing with Network Pruning for Scalable Multi-Agent Deep Reinforcement Learning—0
Resource-Constrained Station-Keeping for Helium Balloons using Reinforcement Learning—0
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Benchmark Results

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