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 8801–8825 of 15113 papers

TitleStatusHype
Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising—0
Real-Time Integrated Dispatching and Idle Fleet Steering with Deep Reinforcement Learning for A Meal Delivery Platform—0
Real-time Local Feature with Global Visual Information Enhancement—0
Real-Time Measurement-Driven Reinforcement Learning Control Approach for Uncertain Nonlinear Systems—0
Real-Time Model Calibration with Deep Reinforcement Learning—0
Real-Time Network-Level Traffic Signal Control: An Explicit Multiagent Coordination Method—0
Real-Time Optimal Design of Experiment for Parameter Identification of Li-Ion Cell Electrochemical Model—0
Real-time Policy Distillation in Deep Reinforcement Learning—0
Real-time scheduling of renewable power systems through planning-based reinforcement learning—0
Real-world challenges for multi-agent reinforcement learning in grid-interactive buildings—0
The Smart Buildings Control Suite: A Diverse Open Source Benchmark to Evaluate and Scale HVAC Control Policies for Sustainability—0
Real-World Fluid Directed Rigid Body Control via Deep Reinforcement Learning—0
Real-World Human-Robot Collaborative Reinforcement Learning—0
Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households—0
Real World Offline Reinforcement Learning with Realistic Data Source—0
Real-world Ride-hailing Vehicle Repositioning using Deep Reinforcement Learning—0
Real-world Video Adaptation with Reinforcement Learning—0
Reannealing of Decaying Exploration Based On Heuristic Measure in Deep Q-Network—0
Rearrangement with Nonprehensile Manipulation Using Deep Reinforcement Learning—0
Reasoning Beyond Limits: Advances and Open Problems for LLMs—0
Reasoning-SQL: Reinforcement Learning with SQL Tailored Partial Rewards for Reasoning-Enhanced Text-to-SQL—0
Reasoning with Exploration: An Entropy Perspective—0
Reasoning With Hierarchical Symbols: Reclaiming Symbolic Policies For Visual Reinforcement Learning—0
Reason-SVG: Hybrid Reward RL for Aha-Moments in Vector Graphics Generation—0
Rebalanced Multimodal Learning with Data-aware Unimodal Sampling—0
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Benchmark Results

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