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 11526–11550 of 15113 papers

TitleStatusHype
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
Show:102550
← PrevPage 462 of 605Next →

Benchmark Results

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