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 9176–9200 of 15113 papers

TitleStatusHype
Dynamic Matching Markets in Power Grid: Concepts and Solution using Deep Reinforcement Learning—0
Survey on reinforcement learning for language processing—0
The Atari Data ScraperCode0
Learn Goal-Conditioned Policy with Intrinsic Motivation for Deep Reinforcement Learning—0
Imperfect also Deserves Reward: Multi-Level and Sequential Reward Modeling for Better Dialog ManagementCode0
Learning to Reweight Imaginary Transitions for Model-Based Reinforcement Learning—0
Jamming-Resilient Path Planning for Multiple UAVs via Deep Reinforcement Learning—0
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
Symmetry reduction for deep reinforcement learning active control of chaotic spatiotemporal dynamics—0
ACERAC: Efficient reinforcement learning in fine time discretization—0
A Bayesian Approach to Reinforcement Learning of Vision-Based Vehicular ControlCode0
Efficient time stepping for numerical integration using reinforcement learningCode0
Non-Asymptotic Analysis for Two Time-scale TDC with General Smooth Function Approximation—0
Generating Multi-type Temporal Sequences to Mitigate Class-imbalanced ProblemCode0
Improving Robustness of Deep Reinforcement Learning Agents: Environment Attack based on the Critic NetworkCode0
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
Risk-Conditioned Distributional Soft Actor-Critic for Risk-Sensitive Navigation—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
Progressive extension of reinforcement learning action dimension for asymmetric assembly tasks—0
Zeus: Efficiently Localizing Actions in Videos using Reinforcement Learning—0
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Benchmark Results

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