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 8426–8450 of 15113 papers

TitleStatusHype
Risk-Conditioned Distributional Soft Actor-Critic for Risk-Sensitive Navigation—0
PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable PhysicsCode1
Zeus: Efficiently Localizing Actions in Videos using Reinforcement Learning—0
Progressive extension of reinforcement learning action dimension for asymmetric assembly tasks—0
Design and implementation of an environment for Learning to Run a Power Network (L2RPN)Code1
Approximate Robust NMPC using Reinforcement Learning—0
Distributed Deep Reinforcement Learning for Collaborative Spectrum Sharing—0
Data-Driven Simulation of Ride-Hailing Services using Imitation and Reinforcement Learning—0
MPC-based Reinforcement Learning for Economic Problems with Application to Battery Storage—0
Temporal-Logic-Based Intermittent, Optimal, and Safe Continuous-Time Learning for Trajectory Tracking—0
C-COMA: A CONTINUAL REINFORCEMENT LEARNING MODEL FOR DYNAMIC MULTIAGENT ENVIRONMENTSCode1
AMP: Adversarial Motion Priors for Stylized Physics-Based Character ControlCode2
A Dual-Critic Reinforcement Learning Framework for Frame-level Bit Allocation in HEVC/H.265—0
Machine Learning Applications in the Routing in Computer Networks—0
NQMIX: Non-monotonic Value Function Factorization for Deep Multi-Agent Reinforcement Learning—0
Distributed Reinforcement Learning for Age of Information Minimization in Real-Time IoT Systems—0
SOLO: Search Online, Learn Offline for Combinatorial Optimization Problems—0
Efficient Transformers in Reinforcement Learning using Actor-Learner Distillation—0
Influencing Reinforcement Learning through Natural Language GuidanceCode0
Deep Reinforcement Learning Powered IRS-Assisted Downlink NOMA—0
A Dynamics Perspective of Pursuit-Evasion Games of Intelligent Agents with the Ability to Learn—0
Reinforcement Learning for Emotional Text-to-Speech Synthesis with Improved Emotion Discriminability—0
Federated Double Deep Q-learning for Joint Delay and Energy Minimization in IoT networks—0
How Are Learned Perception-Based Controllers Impacted by the Limits of Robust Control?Code0
Low Dose Helical CBCT denoising by using domain filtering with deep reinforcement learning—0
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Benchmark Results

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