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 11426–11450 of 15113 papers

TitleStatusHype
End-to-End Vision-Based Adaptive Cruise Control (ACC) Using Deep Reinforcement Learning—0
EgoMap: Projective mapping and structured egocentric memory for Deep RL—0
Pricing commodity swing options—0
Multi-objective Neural Architecture Search via Non-stationary Policy Gradient—0
Facial Feedback for Reinforcement Learning: A Case Study and Offline Analysis Using the TAMER Framework—0
Reducing Non-Normative Text Generation from Language Models—0
Local Policy Optimization for Trajectory-Centric Reinforcement Learning—0
GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal BabblingCode0
Reinforcement Learning Based Vehicle-cell Association Algorithm for Highly Mobile Millimeter Wave Communication—0
On Solving Cooperative MARL Problems with a Few Good Experiences—0
Unsupervisedly Learned Representations: Should the Quest be Over?—0
Lyceum: An efficient and scalable ecosystem for robot learning—0
Emergence of Pragmatics from Referential Game between Theory of Mind AgentsCode0
Cooperative Highway Work Zone Merge Control based on Reinforcement Learning in A Connected and Automated Environment—0
Intelligent Bandwidth Allocation for Latency Management in NG-EPON using Reinforcement Learning Methods—0
Improving Interaction Quality Estimation with BiLSTMs and the Impact on Dialogue Policy Learning—0
Memristor Hardware-Friendly Reinforcement Learning—0
Reinforcement Learning with Probabilistically Complete Exploration—0
Nested-Wasserstein Self-Imitation Learning for Sequence Generation—0
FRESH: Interactive Reward Shaping in High-Dimensional State Spaces using Human Feedback—0
A Survey of Reinforcement Learning Techniques: Strategies, Recent Development, and Future Directions—0
Learning Options from Demonstration using Skill Segmentation—0
BNAS:An Efficient Neural Architecture Search Approach Using Broad Scalable Architecture—0
cube2net: Efficient Query-Specific Network Construction with Data Cube Organization—0
Multi-agent Motion Planning for Dense and Dynamic Environments via Deep Reinforcement Learning—0
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Benchmark Results

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