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 8401–8425 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning Based Controller for Active Heave Compensation—0
Learn Goal-Conditioned Policy with Intrinsic Motivation for Deep Reinforcement Learning—0
The Atari Data ScraperCode0
Imperfect also Deserves Reward: Multi-Level and Sequential Reward Modeling for Better Dialog ManagementCode0
Symmetry reduction for deep reinforcement learning active control of chaotic spatiotemporal dynamics—0
CropGym: a Reinforcement Learning Environment for Crop ManagementCode1
Learning Sampling Policy for Faster Derivative Free Optimization—0
A Reinforcement-Learning-Based Energy-Efficient Framework for Multi-Task Video Analytics Pipeline—0
Inverse Reinforcement Learning: A Control Lyapunov Approach—0
Jamming-Resilient Path Planning for Multiple UAVs via Deep Reinforcement Learning—0
Learning to Reweight Imaginary Transitions for Model-Based Reinforcement Learning—0
Connecting Deep-Reinforcement-Learning-based Obstacle Avoidance with Conventional Global Planners using Waypoint GeneratorsCode1
Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning and Graph Neural NetworksCode1
Efficient time stepping for numerical integration using reinforcement learningCode0
ACERAC: Efficient reinforcement learning in fine time discretization—0
A Bayesian Approach to Reinforcement Learning of Vision-Based Vehicular ControlCode0
A Reinforcement Learning Environment For Job-Shop SchedulingCode1
Arena-Rosnav: Towards Deployment of Deep-Reinforcement-Learning-Based Obstacle Avoidance into Conventional Autonomous Navigation SystemsCode1
Optimal Market Making by Reinforcement LearningCode1
The Value of Planning for Infinite-Horizon Model Predictive ControlCode0
Unsupervised Visual Attention and Invariance for Reinforcement Learning—0
Reinforcement Learning with a Disentangled Universal Value Function for Item Recommendation—0
Generating Multi-type Temporal Sequences to Mitigate Class-imbalanced ProblemCode0
Non-Asymptotic Analysis for Two Time-scale TDC with General Smooth Function Approximation—0
Improving Robustness of Deep Reinforcement Learning Agents: Environment Attack based on the Critic NetworkCode0
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Benchmark Results

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