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 11501–11550 of 15113 papers

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

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