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 10026–10050 of 15113 papers

TitleStatusHype
Curiosity Based Reinforcement Learning on Robot Manufacturing Cell—0
Explaining Conditions for Reinforcement Learning Behaviors from Real and Imagined Data—0
Fault-Aware Robust Control via Adversarial Reinforcement Learning—0
Modality-Buffet for Real-Time Object Detection—0
Reinforcement Learning of Graph Neural Networks for Service Function Chaining—0
Multi-agent Reinforcement Learning Accelerated MCMC on Multiscale Inversion Problem—0
SeekNet: Improved Human Instance Segmentation and Tracking via Reinforcement Learning Based Optimized Robot Relocation—0
REALab: An Embedded Perspective on Tampering—0
PassGoodPool: Joint Passengers and Goods Fleet Management with Reinforcement Learning aided Pricing, Matching, and Route Planning—0
Towards a General Framework for ML-based Self-tuning Databases—0
Value Function Approximations via Kernel Embeddings for No-Regret Reinforcement Learning—0
Towards Learning Controllable Representations of Physical Systems—0
Reward Biased Maximum Likelihood Estimation for Reinforcement Learning—0
Blind Decision Making: Reinforcement Learning with Delayed Observations—0
Distilling a Hierarchical Policy for Planning and Control via Representation and Reinforcement Learning—0
Constrained Model-Free Reinforcement Learning for Process Optimization—0
ACDER: Augmented Curiosity-Driven Experience Replay—0
Analog Circuit Design with Dyna-Style Reinforcement Learning—0
Deep Reinforcement Learning for Cybersecurity Assessment of Wind Integrated Power SystemsCode0
Placement in Integrated Circuits using Cyclic Reinforcement Learning and Simulated Annealing—0
Data-Efficient Learning for Complex and Real-Time Physical Problem Solving using Augmented Simulation—0
RL-QN: A Reinforcement Learning Framework for Optimal Control of Queueing Systems—0
A Geometric Perspective on Self-Supervised Policy Adaptation—0
Active Reinforcement Learning: Observing Rewards at a Cost—0
Critic PI2: Master Continuous Planning via Policy Improvement with Path Integrals and Deep Actor-Critic Reinforcement Learning—0
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Benchmark Results

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