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 3951–4000 of 15113 papers

TitleStatusHype
Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity—0
Derivative-Free Reinforcement Learning: A Review—0
Description Based Text Classification with Reinforcement Learning—0
Design and Comparison of Reward Functions in Reinforcement Learning for Energy Management of Sensor Nodes—0
Design and Development of Spoken Dialogue System in Indic Languages—0
Design and Experimental Test of Datatic Approximate Optimal Filter in Nonlinear Dynamic Systems—0
A Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing—0
Design and Planning of Flexible Mobile Micro-Grids Using Deep Reinforcement Learning—0
Design for a Darwinian Brain: Part 2. Cognitive Architecture—0
Designing a Multi-Objective Reward Function for Creating Teams of Robotic Bodyguards Using Deep Reinforcement Learning—0
Designing a Prospective COVID-19 Therapeutic with Reinforcement Learning—0
Designing Biological Sequences via Meta-Reinforcement Learning and Bayesian Optimization—0
Designing Composites with Target Effective Young's Modulus using Reinforcement Learning—0
Designing Deep Reinforcement Learning for Human Parameter Exploration—0
Designing high-fidelity multi-qubit gates for semiconductor quantum dots through deep reinforcement learning—0
Designing Interpretable Approximations to Deep Reinforcement Learning—0
Coordination of PV Smart Inverters Using Deep Reinforcement Learning for Grid Voltage Regulation—0
Coordination in Adversarial Sequential Team Games via Multi-Agent Deep Reinforcement Learning—0
Assessing Transferability from Simulation to Reality for Reinforcement Learning—0
BATS: Best Action Trajectory Stitching—0
Designing Rewards for Fast Learning—0
Design of Artificial Intelligence Agents for Games using Deep Reinforcement Learning—0
5G MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning—0
Design of Interacting Particle Systems for Fast Linear Quadratic RL—0
Design of Restricted Normalizing Flow towards Arbitrary Stochastic Policy with Computational Efficiency—0
Design Principles of the Hippocampal Cognitive Map—0
Coordination-driven learning in multi-agent problem spaces—0
Coordinating Ride-Pooling with Public Transit using Reward-Guided Conservative Q-Learning: An Offline Training and Online Fine-Tuning Reinforcement Learning Framework—0
DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention—0
Bayesian Bellman Operators—0
Detecting and adapting to crisis pattern with context based Deep Reinforcement Learning—0
Detecting and Adapting to Novelty in Games—0
Assessing the Zero-Shot Capabilities of LLMs for Action Evaluation in RL—0
Detecting Deceptive Reviews using Generative Adversarial Networks—0
Adaptive Stress Testing for Autonomous Vehicles—0
Detecting Worst-case Corruptions via Loss Landscape Curvature in Deep Reinforcement Learning—0
Deterministic Exploration via Stationary Bellman Error Maximization—0
I Cast Detect Thoughts: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons—0
Coordinating Policies Among Multiple Agents via an Intelligent Communication Channel—0
Deterministic Sequencing of Exploration and Exploitation for Reinforcement Learning—0
Coordinating Disaster Emergency Response with Heuristic Reinforcement Learning—0
Deterministic Value-Policy Gradients—0
DETERRENT: Detecting Trojans using Reinforcement Learning—0
A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning—0
ACTRCE: Augmenting Experience via Teacher's Advice For Multi-Goal Reinforcement Learning—0
Developing cooperative policies for multi-stage reinforcement learning tasks—0
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments—0
Developing Multi-Task Recommendations with Long-Term Rewards via Policy Distilled Reinforcement Learning—0
Domain Adaptation for Reinforcement Learning on the Atari—0
Coordinated Reinforcement Learning for Optimizing Mobile Networks—0
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Benchmark Results

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