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 12101–12150 of 15113 papers

TitleStatusHype
Safety-Guided Deep Reinforcement Learning via Online Gaussian Process Estimation—0
Safety-Oriented Pruning and Interpretation of Reinforcement Learning Policies—0
Safety through Permissibility: Shield Construction for Fast and Safe Reinforcement Learning—0
Black-Box Safety Validation of Autonomous Systems: A Multi-Fidelity Reinforcement Learning Approach—0
Safety Verification of Model Based Reinforcement Learning Controllers—0
SaFormer: A Conditional Sequence Modeling Approach to Offline Safe Reinforcement Learning—0
SA-IGA: A Multiagent Reinforcement Learning Method Towards Socially Optimal Outcomes—0
SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learning—0
Saliency-based Sequential Image Attention with Multiset Prediction—0
SaLinA: Sequential Learning of Agents—0
SAMG: State-Action-Aware Offline-to-Online Reinforcement Learning with Offline Model Guidance—0
Sample and Oracle Efficient Reinforcement Learning for MDPs with Linearly-Realizable Value Functions—0
Sample-based Distributional Policy Gradient—0
Sample Complexity Bounds for Two Timescale Value-based Reinforcement Learning Algorithms—0
Sample Complexity of Episodic Fixed-Horizon Reinforcement Learning—0
Sample Complexity of Estimating the Policy Gradient for Nearly Deterministic Dynamical Systems—0
Sample Complexity of Kernel-Based Q-Learning—0
Sample Complexity of Multi-task Reinforcement Learning—0
Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds—0
Sample Complexity of Offline Distributionally Robust Linear Markov Decision Processes—0
Sample Complexity of Policy Gradient Finding Second-Order Stationary Points—0
Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles—0
Sample Complexity Reduction via Policy Difference Estimation in Tabular Reinforcement Learning—0
Sample Efficiency in Sparse Reinforcement Learning: Or Your Money Back—0
Sample-efficient Actor-Critic Reinforcement Learning with Supervised Data for Dialogue Management—0
Sample-efficient Adversarial Imitation Learning from Observation—0
Sample-Efficient and Safe Deep Reinforcement Learning via Reset Deep Ensemble Agents—0
Curriculum Reinforcement Learning for Complex Reward Functions—0
Sample Efficient Deep Reinforcement Learning for Dialogue Systems with Large Action Spaces—0
Sample-efficient Deep Reinforcement Learning for Dialog Control—0
Sample Efficient Deep Reinforcement Learning via Local Planning—0
Sample-efficient Deep Reinforcement Learning with Imaginary Rollouts for Human-Robot Interaction—0
Sample-Efficient, Exploration-Based Policy Optimisation for Routing Problems—0
Sample Efficient Feature Selection for Factored MDPs—0
Physics-informed Imitative Reinforcement Learning for Real-world Driving—0
Sample-Efficient Learning of Nonprehensile Manipulation Policies via Physics-Based Informed State Distributions—0
Sample-Efficient Multi-Agent Reinforcement Learning with Demonstrations for Flocking Control—0
Sample Efficient Myopic Exploration Through Multitask Reinforcement Learning with Diverse Tasks—0
Sample-efficient policy learning in multi-agent Reinforcement Learning via meta-learning—0
Sample efficient Quality Diversity for neural continuous control—0
Sample-Efficient Reinforcement Learning through Transfer and Architectural Priors—0
Sample Efficient Reinforcement Learning by Automatically Learning to Compose Subtasks—0
Sample-Efficient Reinforcement Learning for Linearly-Parameterized MDPs with a Generative Model—0
Reinforcement Learning from Partial Observation: Linear Function Approximation with Provable Sample Efficiency—0
Sample-efficient Reinforcement Learning in Robotic Table Tennis—0
Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity—0
Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information—0
Sample-Efficient Reinforcement Learning Is Feasible for Linearly Realizable MDPs with Limited Revisiting—0
Sample-Efficient Reinforcement Learning of Undercomplete POMDPs—0
Sample-Efficient Reinforcement Learning of Partially Observable Markov Games—0
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Benchmark Results

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