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 2751–2760 of 15113 papers

TitleStatusHype
Algorithms in Multi-Agent Systems: A Holistic Perspective from Reinforcement Learning and Game Theory—0
Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learning—0
Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Primal-Dual Approach—0
Curious iLQR: Resolving Uncertainty in Model-based RL—0
Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment—0
Algorithms for Learning Markov Field Policies—0
Adaptive Energy Management for Real Driving Conditions via Transfer Reinforcement Learning—0
Automated Hybrid Reward Scheduling via Large Language Models for Robotic Skill Learning—0
Automated Gain Control Through Deep Reinforcement Learning for Downstream Radar Object Detection—0
Algorithms for Batch Hierarchical Reinforcement Learning—0
Show:102550
← PrevPage 276 of 1512Next →

Benchmark Results

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