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 10501–10525 of 15113 papers

TitleStatusHype
Model-Reference Reinforcement Learning for Collision-Free Tracking Control of Autonomous Surface Vehicles—0
Playing Catan with Cross-dimensional Neural Network—0
Generative Design by Reinforcement Learning: Enhancing the Diversity of Topology Optimization Designs—0
A Survey on Reinforcement Learning for Combinatorial Optimization—0
DeepSlicing: Deep Reinforcement Learning Assisted Resource Allocation for Network Slicing—0
Forward and inverse reinforcement learning sharing network weights and hyperparameters—0
DRL-Based QoS-Aware Resource Allocation Scheme for Coexistence of Licensed and Unlicensed Users in LTE and Beyond—0
Inverse Reinforcement Learning with Natural Language Goals—0
An adaptive synchronization approach for weights of deep reinforcement learning—0
Chrome Dino Run using Reinforcement Learning—0
Explainability in Deep Reinforcement Learning—0
Autonomous Braking and Throttle System: A Deep Reinforcement Learning Approach for Naturalistic Driving—0
Reinforcement Learning with Quantum Variational CircuitsCode0
Model-Free Optimal Control of Linear Multi-Agent Systems via Decomposition and Hierarchical Approximation—0
Multi-Agent Deep Reinforcement Learning enabled Computation Resource Allocation in a Vehicular Cloud Network—0
Adversary Agnostic Robust Deep Reinforcement Learning—0
Decision-making at Unsignalized Intersection for Autonomous Vehicles: Left-turn Maneuver with Deep Reinforcement Learning—0
Reinforcement Learning with Trajectory Feedback—0
Robust Image Matching By Dynamic Feature Selection—0
Visuomotor Mechanical Search: Learning to Retrieve Target Objects in Clutter—0
Overcoming Model Bias for Robust Offline Deep Reinforcement Learning—0
Model-Based Offline Planning—0
An ocular biomechanics environment for reinforcement learning—0
A Review of Deep Reinforcement Learning for Smart Building Energy Management—0
An Intelligent Control Strategy for buck DC-DC Converter via Deep Reinforcement Learning—0
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Benchmark Results

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