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 4351–4400 of 15113 papers

TitleStatusHype
GoSum: Extractive Summarization of Long Documents by Reinforcement Learning and Graph Organized discourse stateCode0
AlphaSnake: Policy Iteration on a Nondeterministic NP-hard Markov Decision Process—0
A Reinforcement Learning Approach for Process Parameter Optimization in Additive Manufacturing—0
DexPoint: Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulation—0
Learning to Communicate with Intent: An Introduction—0
Solar Power driven EV Charging Optimization with Deep Reinforcement Learning—0
Planning Irregular Object Packing via Hierarchical Reinforcement Learning—0
Minimum information divergence of Q-functions for dynamic treatment resumes—0
Reward Gaming in Conditional Text Generation—0
Model Based Residual Policy Learning with Applications to Antenna Control—0
Addressing the issue of stochastic environments and local decision-making in multi-objective reinforcement learning—0
Data-pooling Reinforcement Learning for Personalized Healthcare Intervention—0
Reinforcement Learning Methods for Wordle: A POMDP/Adaptive Control Approach—0
APT: Adaptive Perceptual quality based camera Tuning using reinforcement learning—0
General Intelligence Requires Rethinking Exploration—0
Explainable Action Advising for Multi-Agent Reinforcement LearningCode0
Contextual Transformer for Offline Meta Reinforcement Learning—0
Agent-State Construction with Auxiliary InputsCode0
Universal Distributional Decision-based Black-box Adversarial Attack with Reinforcement Learning—0
Offline Reinforcement Learning with Adaptive Behavior Regularization—0
Dynamic Collaborative Multi-Agent Reinforcement Learning Communication for Autonomous Drone Reforestation—0
Linear Reinforcement Learning with Ball Structure Action Space—0
Hierarchically Structured Task-Agnostic Continual LearningCode0
Towards Abstractive Timeline Summarisation using Preference-based Reinforcement LearningCode0
Towards Data-Driven Offline Simulations for Online Reinforcement LearningCode1
Reinforcement Learning Based Resource Allocation for Network Slices in O-RAN Midhaul—0
(When) Are Contrastive Explanations of Reinforcement Learning Helpful?—0
NeurIPS 2022 Competition: Driving SMARTS—0
Redeeming Intrinsic Rewards via Constrained OptimizationCode1
Parallel Automatic History Matching Algorithm Using Reinforcement Learning—0
Learning Heterogeneous Agent Cooperation via Multiagent League TrainingCode2
Identifying Coordination in a Cognitive Radar Network -- A Multi-Objective Inverse Reinforcement Learning Approach—0
Multi-Agent Deep Reinforcement Learning for Efficient Passenger Delivery in Urban Air Mobility—0
Goal-Conditioned Reinforcement Learning in the Presence of an Adversary—0
Online Anomalous Subtrajectory Detection on Road Networks with Deep Reinforcement LearningCode1
Deep Reinforcement Learning with Vector Quantized Encoding—0
CACTO: Continuous Actor-Critic with Trajectory Optimization -- Towards global optimality—0
A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, ChallengesCode2
Rewards Encoding Environment Dynamics Improves Preference-based Reinforcement Learning—0
Global and Local Analysis of Interestingness for Competency-Aware Deep Reinforcement LearningCode0
Efficient Domain Coverage for Vehicles with Second-Order Dynamics via Multi-Agent Reinforcement Learning—0
Efficient Deep Reinforcement Learning with Predictive Processing Proximal Policy OptimizationCode0
Gradient Imitation Reinforcement Learning for General Low-Resource Information Extraction—0
Deep Reinforcement Learning Microgrid Optimization Strategy Considering Priority Flexible Demand Side—0
Emergency action termination for immediate reaction in hierarchical reinforcement learning—0
Controlling Commercial Cooling Systems Using Reinforcement Learning—0
Job Scheduling in Datacenters using Constraint Controlled RL—0
When is Realizability Sufficient for Off-Policy Reinforcement Learning?—0
Robust N-1 secure HV Grid Flexibility Estimation for TSO-DSO coordinated Congestion Management with Deep Reinforcement Learning—0
Reinforcement Learning in an Adaptable Chess Environment for Detecting Human-understandable ConceptsCode1
Show:102550
← PrevPage 88 of 303Next →

Benchmark Results

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