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 10151–10175 of 15113 papers

TitleStatusHype
Driver Assistance Eco-driving and Transmission Control with Deep Reinforcement Learning—0
DriverGym: Democratising Reinforcement Learning for Autonomous Driving—0
Driver Modeling through Deep Reinforcement Learning and Behavioral Game Theory—0
Driving Decision and Control for Autonomous Lane Change based on Deep Reinforcement Learning—0
Driving in Real Life with Inverse Reinforcement Learning—0
Driving-Policy Adaptive Safeguard for Autonomous Vehicles Using Reinforcement Learning—0
Driving Tasks Transfer in Deep Reinforcement Learning for Decision-making of Autonomous Vehicles—0
Driving with Style: Inverse Reinforcement Learning in General-Purpose Planning for Automated Driving—0
DRL-Based QoS-Aware Resource Allocation Scheme for Coexistence of Licensed and Unlicensed Users in LTE and Beyond—0
DRL-based Slice Placement Under Non-Stationary Conditions—0
DRL-based Slice Placement under Realistic Network Load Conditions—0
DRL-Clusters: Buffer Management with Clustering based Deep Reinforcement Learning—0
Beyond Sparse Rewards: Enhancing Reinforcement Learning with Language Model Critique in Text Generation—0
DRL: Deep Reinforcement Learning for Intelligent Robot Control -- Concept, Literature, and Future—0
DRL-FAS: A Novel Framework Based on Deep Reinforcement Learning for Face Anti-Spoofing—0
DRL-ISP: Multi-Objective Camera ISP with Deep Reinforcement Learning—0
DR-MPC: Deep Residual Model Predictive Control for Real-world Social Navigation—0
DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning—0
DSADF: Thinking Fast and Slow for Decision Making—0
DSDF: An approach to handle stochastic agents in collaborative multi-agent reinforcement learning—0
DSDF: Coordinated look-ahead strategy in stochastic multi-agent reinforcement learning—0
D-Shape: Demonstration-Shaped Reinforcement Learning via Goal Conditioning—0
DSP: A Differential Spatial Prediction Scheme for Comprehensive real industrial datasets—0
Dual Active Learning for Reinforcement Learning from Human Feedback—0
Dual-Agent Deep Reinforcement Learning for Deformable Face Tracking—0
Show:102550
← PrevPage 407 of 605Next →

Benchmark Results

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