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 4776–4800 of 15113 papers

TitleStatusHype
Real Robot Challenge 2022: Learning Dexterous Manipulation from Offline Data in the Real World—0
A Reinforcement Learning Approach for Performance-aware Reduction in Power Consumption of Data Center Compute NodesCode0
IOB: Integrating Optimization Transfer and Behavior Transfer for Multi-Policy Reuse—0
Insurance pricing on price comparison websites via reinforcement learning—0
ACRE: Actor-Critic with Reward-Preserving ExplorationCode0
Learning to Optimize LSM-trees: Towards A Reinforcement Learning based Key-Value Store for Dynamic Workloads—0
Omega-Regular Reward Machines—0
Neural Categorical Priors for Physics-Based Character Control—0
InTune: Reinforcement Learning-based Data Pipeline Optimization for Deep Recommendation Models—0
CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation—0
A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Control—0
Provably Efficient Algorithm for Nonstationary Low-Rank MDPs—0
Collaborative Wideband Spectrum Sensing and Scheduling for Networked UAVs in UTM Systems—0
Actor-Critic with variable time discretization via sustained actions—0
Characterization of Human Balance through a Reinforcement Learning-based Muscle Controller—0
Exploiting Generalization in Offline Reinforcement Learning via Unseen State Augmentations—0
A Reinforcement Learning-Based Approach to Graph Discovery in D2D-Enabled Federated Learning—0
QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration—0
Nonprehensile Planar Manipulation through Reinforcement Learning with Multimodal Categorical Exploration—0
PARL: A Unified Framework for Policy Alignment in Reinforcement Learning from Human Feedback—0
Bag of Policies for Distributional Deep Exploration—0
Follow the Soldiers with Optimized Single-Shot Multibox Detection and Reinforcement Learning—0
Revisiting a Design Choice in Gradient Temporal Difference Learning—0
BiERL: A Meta Evolutionary Reinforcement Learning Framework via Bilevel OptimizationCode0
End-to-End Reinforcement Learning for Torque Based Variable Height HoppingCode0
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Benchmark Results

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