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 3451–3475 of 15113 papers

TitleStatusHype
CROP: Towards Distributional-Shift Robust Reinforcement Learning using Compact Reshaped Observation ProcessingCode0
Multi-criteria Hardware Trojan Detection: A Reinforcement Learning Approach—0
Can Agents Run Relay Race with Strangers? Generalization of RL to Out-of-Distribution TrajectoriesCode0
Model Extraction Attacks Against Reinforcement Learning Based Controllers—0
Proximal Curriculum for Reinforcement Learning AgentsCode0
Loss- and Reward-Weighting for Efficient Distributed Reinforcement Learning—0
A Closer Look at Reward Decomposition for High-Level Robotic Explanations—0
Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement LearningCode1
What can online reinforcement learning with function approximation benefit from general coverage conditions?—0
Policy Resilience to Environment Poisoning Attacks on Reinforcement Learning—0
On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes—0
Reinforcement Learning with Knowledge Representation and Reasoning: A Brief Survey—0
A Cubic-regularized Policy Newton Algorithm for Reinforcement Learning—0
Reinforcement Learning Approaches for Traffic Signal Control under Missing DataCode0
DEIR: Efficient and Robust Exploration through Discriminative-Model-Based Episodic Intrinsic RewardsCode1
A Review of Symbolic, Subsymbolic and Hybrid Methods for Sequential Decision Making—0
Bridging RL Theory and Practice with the Effective HorizonCode1
End-to-End Policy Gradient Method for POMDPs and Explainable Agents—0
FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing—0
Sample-efficient Model-based Reinforcement Learning for Quantum ControlCode1
Learning and Adapting Agile Locomotion Skills by Transferring Experience—0
Cooperative Multi-Agent Reinforcement Learning for Inventory Management—0
Feasible Policy Iteration for Safe Reinforcement Learning—0
Using Offline Data to Speed Up Reinforcement Learning in Procedurally Generated EnvironmentsCode0
Benchmarking Actor-Critic Deep Reinforcement Learning Algorithms for Robotics Control with Action ConstraintsCode1
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Benchmark Results

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