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 4151–4175 of 15113 papers

TitleStatusHype
Adapting Surprise Minimizing Reinforcement Learning Techniques for Transactive Control—0
Adapting the Exploration Rate for Value-of-Information-Based Reinforcement Learning—0
Adapting the Function Approximation Architecture in Online Reinforcement Learning—0
Adapting User Interfaces with Model-based Reinforcement Learning—0
Adapting World Models with Latent-State Dynamics Residuals—0
Adaptive 3D UI Placement in Mixed Reality Using Deep Reinforcement Learning—0
Adaptive ABAC Policy Learning: A Reinforcement Learning Approach—0
Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations—0
Adaptive Actor-Critic Based Optimal Regulation for Drift-Free Uncertain Nonlinear Systems—0
Adaptive Adversarial Training for Meta Reinforcement Learning—0
Adaptive Aggregation for Safety-Critical Control—0
Adaptive and Multiple Time-scale Eligibility Traces for Online Deep Reinforcement Learning—0
Adaptive Batch Size for Safe Policy Gradients—0
Adaptive Behavior Generation for Autonomous Driving using Deep Reinforcement Learning with Compact Semantic States—0
Deep Reinforcement Learning for Adaptive Caching in Hierarchical Content Delivery Networks—0
ACNMP: Skill Transfer and Task Extrapolation through Learning from Demonstration and Reinforcement Learning via Representation Sharing—0
Adaptive control of a mechatronic system using constrained residual reinforcement learning—0
Adaptive Control of an Inverted Pendulum by a Reinforcement Learning-based LQR Method—0
Adaptive Control of Differentially Private Linear Quadratic Systems—0
Adaptive Coordination Offsets for Signalized Arterial Intersections using Deep Reinforcement Learning—0
Adaptive Decision Making at the Intersection for Autonomous Vehicles Based on Skill Discovery—0
Adaptive Dialog Policy Learning with Hindsight and User Modeling—0
Adaptive Discounting of Training Time Attacks—0
Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization—0
Policy Zooming: Adaptive Discretization-based Infinite-Horizon Average-Reward Reinforcement Learning—0
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Benchmark Results

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