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 11301–11350 of 15113 papers

TitleStatusHype
Optimizing Multiagent Cooperation via Policy Evolution and Shared Experiences—0
Variational Imitation Learning with Diverse-quality DemonstrationsCode1
Responsive Safety in Reinforcement Learning—0
OPtions as REsponses: Grounding behavioural hierarchies in multi-agent reinforcement learning—0
The Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary Neural Circuits—0
Reinforcement Learning with Differential Privacy—0
SVQN: Sequential Variational Soft Q-Learning Networks—0
Long-Term Visitation Value for Deep Exploration in Sparse Reward Reinforcement LearningCode0
Meta Reinforcement Learning with Autonomous Inference of Subtask DependenciesCode1
Reinforcement Learning with Goal-Distance Gradient—0
Deep Reinforcement Learning with Implicit Human Feedback—0
Learning Representations in Reinforcement Learning: an Information Bottleneck Approach—0
Deep Randomized Least Squares Value Iteration—0
Improving the Generalization of Visual Navigation Policies using Invariance Regularization—0
Way Off-Policy Batch Deep Reinforcement Learning of Human Preferences in Dialog—0
Information Theoretic Model Predictive Q-Learning—0
The Gambler's Problem and Beyond—0
Uncertainty-Based Out-of-Distribution Classification in Deep Reinforcement Learning—0
Reward-Conditioned PoliciesCode0
PAC Confidence Sets for Deep Neural Networks via Calibrated PredictionCode1
World Programs for Model-Based Learning and Planning in Compositional State and Action Spaces—0
Deep Reinforced Self-Attention Masks for Abstractive Summarization (DR.SAS)—0
A New Framework for Query Efficient Active Imitation Learning—0
Computational model discovery with reinforcement learning—0
Augmented Replay Memory in Reinforcement Learning With Continuous Control—0
Individual specialization in multi-task environments with multiagent reinforcement learners—0
Real-time Policy Distillation in Deep Reinforcement Learning—0
Speeding up reinforcement learning by combining attention and agency features—0
Weak Supervision for Fake News Detection via Reinforcement LearningCode0
SLM Lab: A Comprehensive Benchmark and Modular Software Framework for Reproducible Deep Reinforcement LearningCode0
Evolution Strategies Converges to Finite Differences—0
Deep reinforcement learning for complex evaluation of one-loop diagrams in quantum field theory—0
Crowdfunding Dynamics Tracking: A Reinforcement Learning Approach—0
Quantum Logic Gate Synthesis as a Markov Decision Process—0
Quasi-Newton Trust Region Policy Optimization—0
Learning to Combat Compounding-Error in Model-Based Reinforcement Learning—0
Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature AttributionCode0
A Survey of Deep Reinforcement Learning in Video Games—0
Hamilton-Jacobi-Bellman Equations for Q-Learning in Continuous Time—0
Direct and indirect reinforcement learning—0
Discrete and Continuous Action Representation for Practical RL in Video GamesCode0
Learning to Navigate Using Mid-Level Visual PriorsCode0
Monte-Carlo Tree Search for Policy Optimization—0
Parameterized Indexed Value Function for Efficient Exploration in Reinforcement LearningCode0
Towards Practical Multi-Object Manipulation using Relational Reinforcement LearningCode0
Variational Recurrent Models for Solving Partially Observable Control TasksCode0
Energy-Aware Multi-Server Mobile Edge Computing: A Deep Reinforcement Learning Approach—0
Predictive Coding for Boosting Deep Reinforcement Learning with Sparse Rewards—0
Online Reinforcement Learning of Optimal Threshold Policies for Markov Decision Processes—0
Can Agents Learn by Analogy? An Inferable Model for PAC Reinforcement LearningCode0
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Benchmark Results

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