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

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Showing 9151–9200 of 15113 papers

TitleStatusHype
Learning on a Budget via Teacher ImitationCode0
Action Advising with Advice Imitation in Deep Reinforcement LearningCode0
Safe Exploration in Model-based Reinforcement Learning using Control Barrier Functions—0
MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scale—0
Rule-Based Reinforcement Learning for Efficient Robot Navigation with Space Reduction—0
Multi-Agent Reinforcement Learning Based Coded Computation for Mobile Ad Hoc Computing—0
Predictor-Corrector(PC) Temporal Difference(TD) Learning (PCTD)—0
Discover the Hidden Attack Path in Multi-domain Cyberspace Based on Reinforcement Learning—0
Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills—0
An L^2 Analysis of Reinforcement Learning in High Dimensions with Kernel and Neural Network Approximation—0
Joint Attention for Multi-Agent Coordination and Social Learning—0
A Novel Approach to Curiosity and Explainable Reinforcement Learning via Interpretable Sub-GoalsCode0
GAN-Based Interactive Reinforcement Learning from Demonstration and Human Evaluative Feedback—0
GridToPix: Training Embodied Agents with Minimal Supervision—0
Safe Continuous Control with Constrained Model-Based Policy OptimizationCode0
Visual Comfort Aware-Reinforcement Learning for Depth Adjustment of Stereoscopic 3D Images—0
Optimizing the Long-Term Average Reward for Continuing MDPs: A Technical Report—0
Two-stage training algorithm for AI robot soccer—0
Subgoal-based Reward Shaping to Improve Efficiency in Reinforcement Learning—0
Reinforcement learning for Admission Control in 5G Wireless Networks—0
Reward Shaping with Dynamic Trajectory Aggregation—0
Reward Shaping with Subgoals for Social Navigation—0
Data-Driven Reinforcement Learning for Virtual Character Animation Control—0
Bi-level Off-policy Reinforcement Learning for Volt/VAR Control Involving Continuous and Discrete Devices—0
Deep Reinforcement Learning Based Controller for Active Heave Compensation—0
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