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

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Showing 3901–3950 of 15113 papers

TitleStatusHype
Planning Multiple Epidemic Interventions with Reinforcement LearningCode0
PAC-Bayesian Soft Actor-Critic LearningCode0
Improved Regret for Efficient Online Reinforcement Learning with Linear Function Approximation—0
V2N Service Scaling with Deep Reinforcement Learning—0
Regret Bounds for Markov Decision Processes with Recursive Optimized Certainty Equivalents—0
Guiding Online Reinforcement Learning with Action-Free Offline PretrainingCode1
Transferring Multiple Policies to Hotstart Reinforcement Learning in an Air Compressor Management Problem—0
Hierarchical Programmatic Reinforcement Learning via Learning to Compose Programs—0
Autonomous Satellite Docking via Adaptive Optimal Output Rregulation: A Reinforcement Learning Approach—0
A Deep Reinforcement Learning Framework for Optimizing Congestion Control in Data Centers—0
Sample Efficient Deep Reinforcement Learning via Local Planning—0
SaFormer: A Conditional Sequence Modeling Approach to Offline Safe Reinforcement Learning—0
Do Embodied Agents Dream of Pixelated Sheep: Embodied Decision Making using Language Guided World ModellingCode1
Turbulence control in plane Couette flow using low-dimensional neural ODE-based models and deep reinforcement learning—0
Constrained Policy Optimization with Explicit Behavior Density for Offline Reinforcement LearningCode0
Beyond Exponentially Fast Mixing in Average-Reward Reinforcement Learning via Multi-Level Monte Carlo Actor-Critic—0
Towards Learning Rubik's Cube with N-tuple-based Reinforcement Learning—0
STEERING: Stein Information Directed Exploration for Model-Based Reinforcement Learning—0
SNeRL: Semantic-aware Neural Radiance Fields for Reinforcement Learning—0
Exploring Deep Reinforcement Learning for Holistic Smart Building Control—0
A Memory Efficient Deep Reinforcement Learning Approach For Snake Game Autonomous Agents—0
Outcome-directed Reinforcement Learning by Uncertainty & Temporal Distance-Aware Curriculum Goal GenerationCode1
Solving Richly Constrained Reinforcement Learning through State Augmentation and Reward Penalties—0
Reinforcement Learning from Diverse Human Preferences—0
Single-Trajectory Distributionally Robust Reinforcement Learning—0
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