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 8676–8700 of 15113 papers

TitleStatusHype
Reinforcement learning approach for resource allocation in humanitarian logistics—0
Reinforcement Learning of Implicit and Explicit Control Flow in Instructions—0
A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement LearningCode1
Twin actor twin delayed deep deterministic policy gradient (TATD3) learning for batch process control—0
Bias-reduced Multi-step Hindsight Experience Replay for Efficient Multi-goal Reinforcement Learning—0
Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods—0
Adaptive Load Shedding for Grid Emergency Control via Deep Reinforcement Learning—0
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning—0
On The Effect of Auxiliary Tasks on Representation Dynamics—0
Modular Object-Oriented Games: A Task Framework for Reinforcement Learning, Psychology, and NeuroscienceCode1
Where to go next: Learning a Subgoal Recommendation Policy for Navigation Among Pedestrians—0
No-Regret Reinforcement Learning with Heavy-Tailed Rewards—0
Beyond Fine-Tuning: Transferring Behavior in Reinforcement Learning—0
Deep Reinforcement Learning for Safe Landing Site Selection with Concurrent Consideration of Divert Maneuvers—0
Credit Assignment with Meta-Policy Gradient for Multi-Agent Reinforcement Learning—0
FIXAR: A Fixed-Point Deep Reinforcement Learning Platform with Quantization-Aware Training and Adaptive Parallelism—0
Information Directed Reward Learning for Reinforcement LearningCode1
Fast Approximate Solutions using Reinforcement Learning for Dynamic Capacitated Vehicle Routing with Time Windows—0
Learning Emergent Discrete Message Communication for Cooperative Reinforcement Learning—0
Annotating Motion Primitives for Simplifying Action Search in Reinforcement Learning—0
Combining Off and On-Policy Training in Model-Based Reinforcement Learning—0
Towards Safe Continuing Task Reinforcement Learning—0
Modular Deep Reinforcement Learning for Continuous Motion Planning with Temporal LogicCode0
PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference LearningCode0
PFRL: Pose-Free Reinforcement Learning for 6D Pose Estimation—0
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Benchmark Results

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