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 5676–5700 of 15113 papers

TitleStatusHype
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
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Benchmark Results

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