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 14001–14050 of 15113 papers

TitleStatusHype
Back to Basics: Benchmarking Canonical Evolution Strategies for Playing AtariCode0
Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising—0
Verifying Controllers Against Adversarial Examples with Bayesian OptimizationCode0
Ranking Sentences for Extractive Summarization with Reinforcement LearningCode0
Weighted Double Deep Multiagent Reinforcement Learning in Stochastic Cooperative Environments—0
Structured Control Nets for Deep Reinforcement LearningCode0
Diverse Exploration for Fast and Safe Policy Improvement—0
An Analysis of Categorical Distributional Reinforcement Learning—0
Variational Inference for Policy Gradient—0
Continual Reinforcement Learning with Complex Synapses—0
Fourier Policy Gradients—0
Accelerated Primal-Dual Policy Optimization for Safe Reinforcement Learning—0
Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning—0
Estimating scale-invariant future in continuous time—0
Efficient Collaborative Multi-Agent Deep Reinforcement Learning for Large-Scale Fleet ManagementCode0
Improving Mild Cognitive Impairment Prediction via Reinforcement Learning and Dialogue Simulation—0
Bridging Cognitive Programs and Machine Learning—0
Monte Carlo Q-learning for General Game PlayingCode0
Reactive Reinforcement Learning in Asynchronous Environments—0
Modeling the Formation of Social Conventions from Embodied Real-Time Interactions—0
Prioritized Sweeping Neural DynaQ with Multiple Predecessors, and Hippocampal Replays—0
Reinforcement Learning from Imperfect Demonstrations—0
GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning AlgorithmsCode0
From Gameplay to Symbolic Reasoning: Learning SAT Solver Heuristics in the Style of Alpha(Go) ZeroCode0
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning—0
A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems—0
Evolved Policy GradientsCode0
Efficient Exploration through Bayesian Deep Q-NetworksCode0
Progressive Reinforcement Learning with Distillation for Multi-Skilled Motion Control—0
M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search—0
Reinforcement Learning for Solving the Vehicle Routing ProblemCode0
Efficient Model-Based Deep Reinforcement Learning with Variational State TabulationCode0
Reinforcement Learning with Wasserstein Distance Regularisation, with Applications to Multipolicy Learning—0
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement LearningCode0
Sample Efficient Deep Reinforcement Learning for Dialogue Systems with Large Action Spaces—0
More Robust Doubly Robust Off-policy Evaluation—0
Beyond the One Step Greedy Approach in Reinforcement Learning—0
Balancing Two-Player Stochastic Games with Soft Q-Learning—0
Learning and Querying Fast Generative Models for Reinforcement Learning—0
Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsis—0
From Game-theoretic Multi-agent Log Linear Learning to Reinforcement Learning—0
A Critical Investigation of Deep Reinforcement Learning for NavigationCode0
Deep Reinforcement Learning for Image Hashing—0
Efficient collective swimming by harnessing vortices through deep reinforcement learning—0
Decomposition Methods with Deep Corrections for Reinforcement LearningCode0
Shared Autonomy via Deep Reinforcement LearningCode0
Coordinated Exploration in Concurrent Reinforcement Learning—0
Multimodal Sentiment Analysis with Word-Level Fusion and Reinforcement LearningCode0
Multi-task Learning for Continuous Control—0
Elements of Effective Deep Reinforcement Learning towards Tactical Driving Decision Making—0
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Benchmark Results

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