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 10351–10400 of 15113 papers

TitleStatusHype
Accelerated Deep Reinforcement Learning Based Load Shedding for Emergency Voltage Control—0
Efficient Sampling-Based Maximum Entropy Inverse Reinforcement Learning with Application to Autonomous Driving—0
dm_control: Software and Tasks for Continuous Control—0
Graph Neural Networks and Reinforcement Learning for Behavior Generation in Semantic EnvironmentsCode1
Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret—0
Sample-Efficient Reinforcement Learning of Undercomplete POMDPs—0
QTRAN++: Improved Value Transformation for Cooperative Multi-Agent Reinforcement Learning—0
Safe Reinforcement Learning via Curriculum InductionCode1
Provably Efficient Causal Reinforcement Learning with Confounded Observational Data—0
Near-Optimal Reinforcement Learning with Self-Play—0
Learning with AMIGo: Adversarially Motivated Intrinsic GoalsCode1
Ecological Reinforcement Learning—0
Constrained Combinatorial Optimization with Reinforcement Learning—0
Hierarchical Reinforcement Learning for Deep Goal Reasoning: An Expressiveness Analysis—0
Reinforcement Learning for Mean Field Games with Strategic Complementarities—0
Gradient-EM Bayesian Meta-learning—0
Automated Optical Multi-layer Design via Deep Reinforcement LearningCode0
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning—0
Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement LearningCode1
Off-Policy Self-Critical Training for Transformer in Visual Paragraph Generation—0
Towards Tractable Optimism in Model-Based Reinforcement Learning—0
Robust Reinforcement Learning using Least Squares Policy Iteration with Provable Performance Guarantees—0
Entropic Risk Constrained Soft-Robust Policy Optimization—0
Accelerating Safe Reinforcement Learning with Constraint-mismatched Policies—0
Langevin Dynamics for Adaptive Inverse Reinforcement Learning of Stochastic Gradient Algorithms—0
Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement LearningCode1
Deep Implicit Coordination Graphs for Multi-agent Reinforcement LearningCode1
FISAR: Forward Invariant Safe Reinforcement Learning with a Deep Neural Network-Based Optimize—0
Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian ProcessesCode1
On Reward-Free Reinforcement Learning with Linear Function Approximation—0
NROWAN-DQN: A Stable Noisy Network with Noise Reduction and Online Weight Adjustment for Exploration—0
A Reinforcement Learning Approach for Transient Control of Liquid Rocket Engines—0
Learn to Earn: Enabling Coordination within a Ride Hailing Fleet—0
WD3: Taming the Estimation Bias in Deep Reinforcement Learning—0
Provably adaptive reinforcement learning in metric spaces—0
Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement LearningCode1
Cooperative Multi-Agent Reinforcement Learning with Partial Observations—0
Efficient Ridesharing Dispatch Using Multi-Agent Reinforcement LearningCode0
FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs—0
Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning—0
Deep Reinforcement Learning amidst Lifelong Non-Stationarity—0
Distributed Value Function Approximation for Collaborative Multi-Agent Reinforcement Learning—0
DREAM: Deep Regret minimization with Advantage baselines and Model-free learningCode1
Learning Invariant Representations for Reinforcement Learning without ReconstructionCode1
Converting Biomechanical Models from OpenSim to MuJoCoCode1
Eco-Vehicular Edge Networks for Connected Transportation: A Distributed Multi-Agent Reinforcement Learning Approach—0
Green Simulation Assisted Reinforcement Learning with Model Risk for Biomanufacturing Learning and ControlCode0
Introduction to Machine Learning for Accelerator Physics—0
Learning to Track Dynamic Targets in Partially Known EnvironmentsCode1
Deep Reinforcement Learning Controller for 3D Path-following and Collision Avoidance by Autonomous Underwater Vehicles—0
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Benchmark Results

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