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 3926–3950 of 15113 papers

TitleStatusHype
Generalized Munchausen Reinforcement Learning using Tsallis KL Divergence—0
Modeling human road crossing decisions as reward maximization with visual perception limitations—0
Improving Behavioural Cloning with Positive Unlabeled Learning—0
Certifiably Robust Reinforcement Learning through Model-Based Abstract Interpretation—0
Model-based Offline Reinforcement Learning with Local Misspecification—0
Communication-Efficient Collaborative Regret Minimization in Multi-Armed Bandits—0
Learning to Generate All Feasible Actions—0
On the Global Convergence of Risk-Averse Policy Gradient Methods with Expected Conditional Risk Measures—0
FedHQL: Federated Heterogeneous Q-Learning—0
Automatic Intrinsic Reward Shaping for Exploration in Deep Reinforcement LearningCode3
Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons—0
Deep Laplacian-based Options for Temporally-Extended ExplorationCode1
Learning from Multiple Independent Advisors in Multi-agent Reinforcement LearningCode0
Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement LearningCode0
Which Experiences Are Influential for Your Agent? Policy Iteration with Turn-over DropoutCode0
Trust Region-Based Safe Distributional Reinforcement Learning for Multiple ConstraintsCode1
A Deep Neural Network Algorithm for Linear-Quadratic Portfolio Optimization with MGARCH and Small Transaction Costs—0
Distributed Control of Partial Differential Equations Using Convolutional Reinforcement LearningCode1
Select and Trade: Towards Unified Pair Trading with Hierarchical Reinforcement LearningCode1
A Novel Deep Reinforcement Learning-based Approach for Enhancing Spectral Efficiency of IRS-assisted Wireless Systems—0
SMART: Self-supervised Multi-task pretrAining with contRol Transformers—0
Explainable Deep Reinforcement Learning: State of the Art and Challenges—0
Autonomous particles—0
AutoCost: Evolving Intrinsic Cost for Zero-violation Reinforcement Learning—0
ASQ-IT: Interactive Explanations for Reinforcement-Learning Agents—0
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Benchmark Results

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