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 10001–10025 of 15113 papers

TitleStatusHype
Reinforcement learning with distance-based incentive/penalty (DIP) updates for highly constrained industrial control systems—0
Distributed Deep Reinforcement Learning: An Overview—0
Double Meta-Learning for Data Efficient Policy Optimization in Non-Stationary Environments—0
On the Convergence of Reinforcement Learning in Nonlinear Continuous State Space Problems—0
Policy Teaching in Reinforcement Learning via Environment Poisoning Attacks—0
MRAC-RL: A Framework for On-Line Policy Adaptation Under Parametric Model Uncertainty—0
Bridging Scene Understanding and Task Execution with Flexible Simulation Environments—0
Model-based Reinforcement Learning for Continuous Control with Posterior SamplingCode0
Delay Constrained Buffer-Aided Relay Selection in the Internet of Things with Decision-Assisted Reinforcement Learning—0
Deep reinforcement learning for feedback control in a collective flashing ratchetCode0
Energy Aware Deep Reinforcement Learning Scheduling for Sensors Correlated in Time and Space—0
Parrot: Data-Driven Behavioral Priors for Reinforcement Learning—0
Provable Multi-Objective Reinforcement Learning with Generative Models—0
Online Model Selection for Reinforcement Learning with Function Approximation—0
Weighted Entropy Modification for Soft Actor-Critic—0
Indoor Point-to-Point Navigation with Deep Reinforcement Learning and Ultra-wideband—0
Inverse Reinforcement Learning via Matching of Optimality Profiles—0
Experimental Study on Reinforcement Learning-based Control of an Acrobot—0
LAVA: Latent Action Spaces via Variational Auto-encoding for Dialogue Policy Optimization—0
Counterfactual Credit Assignment in Model-Free Reinforcement Learning—0
Deep Reinforcement Learning and Permissioned Blockchain for Content Caching in Vehicular Edge Computing and Networks—0
Deep Reinforcement Learning for Stochastic Computation Offloading in Digital Twin Networks—0
Leveraging the Variance of Return Sequences for Exploration Policy—0
Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization—0
C-Learning: Learning to Achieve Goals via Recursive Classification—0
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Benchmark Results

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