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 7001–7025 of 15113 papers

TitleStatusHype
C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks—0
ReLAX: Reinforcement Learning Agent eXplainer for Arbitrary Predictive ModelsCode0
A Versatile and Efficient Reinforcement Learning Framework for Autonomous DrivingCode1
Reinforcement Learning for Process Control with Application in Semiconductor Manufacturing—0
Patient level simulation and reinforcement learning to discover novel strategies for treating ovarian cancer—0
Model-based Reinforcement Learning for Service Mesh Fault Resiliency in a Web Application-level—0
Reinforcement Learning Based Optimal Camera Placement for Depth Observation of Indoor Scenes—0
Off-Dynamics Inverse Reinforcement Learning from Hetero-Domain—0
Sequential Voting with Relational Box Fields for Active Object DetectionCode1
Anti-Concentrated Confidence Bonuses for Scalable Exploration—0
Is High Variance Unavoidable in RL? A Case Study in Continuous Control—0
Efficient Robotic Manipulation Through Offline-to-Online Reinforcement Learning and Goal-Aware State Information—0
Deep Reinforcement Learning for Online Control of Stochastic Partial Differential Equations—0
Can Q-learning solve Multi Armed Bantids?—0
LOA: Logical Optimal Actions for Text-based Interaction GamesCode1
Neuro-Symbolic Reinforcement Learning with First-Order Logic—0
Playing 2048 With Reinforcement LearningCode0
Computationally Efficient Safe Reinforcement Learning for Power Systems—0
Feedback Linearization of Car Dynamics for Racing via Reinforcement Learning—0
Distributed Reinforcement Learning for Privacy-Preserving Dynamic Edge Caching—0
Hierarchical Skills for Efficient ExplorationCode1
Socialbots on Fire: Modeling Adversarial Behaviors of Socialbots via Multi-Agent Hierarchical Reinforcement Learning—0
More Efficient Exploration with Symbolic Priors on Action Sequence Equivalences—0
Transferring Reinforcement Learning for DC-DC Buck Converter Control via Duty Ratio Mapping: From Simulation to Implementation—0
Improved cooperation by balancing exploration and exploitation in intertemporal social dilemma tasks—0
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Benchmark Results

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