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 9301–9325 of 15113 papers

TitleStatusHype
Warren at SemEval-2020 Task 4: ALBERT and Multi-Task Learning for Commonsense Validation—0
Revisiting Maximum Entropy Inverse Reinforcement Learning: New Perspectives and AlgorithmsCode1
Obtain Employee Turnover Rate and Optimal Reduction Strategy Based On Neural Network and Reinforcement Learning—0
Assessing and Accelerating Coverage in Deep Reinforcement Learning—0
EcoLight: Intersection Control in Developing Regions Under Extreme Budget and Network Constraints—0
A Local Temporal Difference Code for Distributional Reinforcement Learning—0
Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory—0
A new convergent variant of Q-learning with linear function approximation—0
Almost Optimal Model-Free Reinforcement Learningvia Reference-Advantage Decomposition—0
Promoting Stochasticity for Expressive Policies via a Simple and Efficient Regularization Method—0
R-learning in actor-critic model offers a biologically relevant mechanism for sequential decision-making—0
MOReL: Model-Based Offline Reinforcement Learning—0
Non-Crossing Quantile Regression for Distributional Reinforcement Learning—0
On the Stability and Convergence of Robust Adversarial Reinforcement Learning: A Case Study on Linear Quadratic Systems—0
Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations—0
Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement Learning—0
RL Unplugged: A Collection of Benchmarks for Offline Reinforcement LearningCode0
On the Convergence of Smooth Regularized Approximate Value Iteration Schemes—0
Security Analysis of Safe and Seldonian Reinforcement Learning Algorithms—0
Reinforcement Learning for Control with Multiple Frequencies—0
Robust Multi-Agent Reinforcement Learning with Model Uncertainty—0
Online Decision Based Visual Tracking via Reinforcement Learning—0
Provably Good Batch Off-Policy Reinforcement Learning Without Great Exploration—0
On Efficiency in Hierarchical Reinforcement Learning—0
Is Long Horizon RL More Difficult Than Short Horizon RL?—0
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Benchmark Results

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