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 4801–4850 of 15113 papers

TitleStatusHype
SAFER: Safe Collision Avoidance using Focused and Efficient Trajectory Search with Reinforcement Learning—0
On Efficient Reinforcement Learning for Full-length Game of StarCraft IICode2
Quantification before Selection: Active Dynamics Preference for Robust Reinforcement Learning—0
Unified Algorithms for RL with Decision-Estimation Coefficients: PAC, Reward-Free, Preference-Based Learning, and Beyond—0
Minimizing Human Assistance: Augmenting a Single Demonstration for Deep Reinforcement Learning—0
Pretraining the Vision Transformer using self-supervised methods for vision based Deep Reinforcement LearningCode0
Parallel Reinforcement Learning Simulation for Visual Quadrotor Navigation—0
Reinforcement Learning in Computing and Network Convergence Orchestration—0
Computational Discovery of Energy-Efficient Heat Treatment for Microstructure Design using Deep Reinforcement Learning—0
Identifiability and generalizability from multiple experts in Inverse Reinforcement LearningCode0
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments—0
An Investigation of the Bias-Variance Tradeoff in Meta-GradientsCode0
Hierarchical Decentralized Deep Reinforcement Learning Architecture for a Simulated Four-Legged AgentCode0
Learning from Symmetry: Meta-Reinforcement Learning with Symmetrical Behaviors and Language Instructions—0
ECSAS: Exploring Critical Scenarios from Action Sequence in Autonomous Driving—0
Evaluation of Look-ahead Economic Dispatch Using Reinforcement Learning—0
LCRL: Certified Policy Synthesis via Logically-Constrained Reinforcement LearningCode1
Lamarckian Platform: Pushing the Boundaries of Evolutionary Reinforcement Learning towards Asynchronous Commercial Games—0
Hierarchical Decision Transformer—0
Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks—0
Revisiting Discrete Soft Actor-CriticCode1
On the Convergence Theory of Meta Reinforcement Learning with Personalized Policies—0
Model-Free Reinforcement Learning for Asset Allocation—0
Towards Task-Prioritized Policy Composition—0
Optimizing Crop Management with Reinforcement Learning and Imitation Learning—0
Soft Action Priors: Towards Robust Policy Transfer—0
Locally Constrained Representations in Reinforcement Learning—0
Macro-Action-Based Multi-Agent/Robot Deep Reinforcement Learning under Partial Observability—0
Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning—0
Graph Value Iteration—0
Deep Q-Network for AI Soccer—0
IRS Assisted NOMA Aided Mobile Edge Computing with Queue Stability: Heterogeneous Multi-Agent Reinforcement Learning—0
A Spiking Neural Network Learning Markov Chain—0
A Joint Imitation-Reinforcement Learning Framework for Reduced Baseline RegretCode0
A Deep Reinforcement Learning-Based Charging Scheduling Approach with Augmented Lagrangian for Electric Vehicle—0
Transferring Knowledge for Reinforcement Learning in Contact-Rich Manipulation—0
Meta-Reinforcement Learning for Adaptive Control of Second Order Systems—0
MAN: Multi-Action Networks LearningCode1
Safe reinforcement learning control for continuous-time nonlinear systems without a backup controller—0
Rewarding Episodic Visitation Discrepancy for Exploration in Reinforcement Learning—0
Measuring Interventional Robustness in Reinforcement LearningCode0
MSVIPER: Improved Policy Distillation for Reinforcement-Learning-Based Robot Navigation—0
BOME! Bilevel Optimization Made Easy: A Simple First-Order ApproachCode1
Age of Semantics in Cooperative Communications: To Expedite Simulation Towards Real via Offline Reinforcement Learning—0
Enforcing the consensus between Trajectory Optimization and Policy Learning for precise robot control—0
A Transferable and Automatic Tuning of Deep Reinforcement Learning for Cost Effective Phishing Detection—0
Active Predicting Coding: Brain-Inspired Reinforcement Learning for Sparse Reward Robotic Control Problems—0
Learning to Walk by Steering: Perceptive Quadrupedal Locomotion in Dynamic EnvironmentsCode1
"Guess what I'm doing": Extending legibility to sequential decision tasks—0
Latent Plans for Task-Agnostic Offline Reinforcement LearningCode1
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Benchmark Results

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