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 9426–9450 of 15113 papers

TitleStatusHype
Reward Biased Maximum Likelihood Estimation for Reinforcement Learning—0
Blind Decision Making: Reinforcement Learning with Delayed Observations—0
Constrained Model-Free Reinforcement Learning for Process Optimization—0
Learning Associative Inference Using Fast Weight MemoryCode1
Analog Circuit Design with Dyna-Style Reinforcement Learning—0
Distilling a Hierarchical Policy for Planning and Control via Representation and Reinforcement Learning—0
ACDER: Augmented Curiosity-Driven Experience Replay—0
Hierarchical clustering in particle physics through reinforcement learningCode1
Deep Reinforcement Learning for Cybersecurity Assessment of Wind Integrated Power SystemsCode0
CDT: Cascading Decision Trees for Explainable Reinforcement LearningCode1
Tonic: A Deep Reinforcement Learning Library for Fast Prototyping and BenchmarkingCode1
Placement in Integrated Circuits using Cyclic Reinforcement Learning and Simulated Annealing—0
PLAS: Latent Action Space for Offline Reinforcement LearningCode1
Data-Efficient Learning for Complex and Real-Time Physical Problem Solving using Augmented Simulation—0
A Geometric Perspective on Self-Supervised Policy Adaptation—0
SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object ManipulationCode1
RL-QN: A Reinforcement Learning Framework for Optimal Control of Queueing Systems—0
Reinforcement Learning Control of a Biomechanical Model of the Upper Extremity—0
Robust Quadruped Jumping via Deep Reinforcement Learning—0
Query-based Targeted Action-Space Adversarial Policies on Deep Reinforcement Learning AgentsCode0
Phoebe: Reuse-Aware Online Caching with Reinforcement Learning for Emerging Storage Models—0
Scaffolding Reflection in Reinforcement Learning Framework for Confinement Escape Problem—0
Reinforcement Learning Control of Constrained Dynamic Systems with Uniformly Ultimate Boundedness Stability Guarantee—0
Robotic self-representation improves manipulation skills and transfer learning—0
ROLL: Visual Self-Supervised Reinforcement Learning with Object ReasoningCode1
Show:102550
← PrevPage 378 of 605Next →

Benchmark Results

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