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 8626–8650 of 15113 papers

TitleStatusHype
Fully Autonomous Real-World Reinforcement Learning with Applications to Mobile Manipulation—0
Packet Routing with Graph Attention Multi-agent Reinforcement Learning—0
Value-Based Reinforcement Learning for Continuous Control Robotic Manipulation in Multi-Task Sparse Reward Settings—0
Autonomous Reinforcement Learning via Subgoal Curricula—0
Reinforcement Learning with Formal Performance Metrics for Quadcopter Attitude Control under Non-nominal Contexts—0
Human-Level Reinforcement Learning through Theory-Based Modeling, Exploration, and Planning—0
Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment—0
Asynchronous Distributed Reinforcement Learning for LQR Control via Zeroth-Order Block Coordinate Descent—0
Playtesting: What is Beyond Personas—0
Reinforced Imitation Learning by Free Energy Principle—0
DR2L: Surfacing Corner Cases to Robustify Autonomous Driving via Domain Randomization Reinforcement Learning—0
Cooperative Exploration for Multi-Agent Deep Reinforcement Learning—0
Learning Quadruped Locomotion Policies using Logical Rules—0
A reinforcement learning approach to resource allocation in genomic selection—0
Bayesian Controller Fusion: Leveraging Control Priors in Deep Reinforcement Learning for Robotics—0
A Deep Reinforcement Learning Approach for Fair Traffic Signal Control—0
Reinforcement Learning Agent Training with Goals for Real World Tasks—0
Toward Collaborative Reinforcement Learning Agents that Communicate Through Text-Based Natural LanguageCode0
Proximal Policy Optimization for Tracking Control Exploiting Future Reference Information—0
Improved Reinforcement Learning in Cooperative Multi-agent Environments Using Knowledge Transfer—0
Learning Altruistic Behaviours in Reinforcement Learning without External Rewards—0
Decoupled Reinforcement Learning to Stabilise Intrinsically-Motivated Exploration—0
Constraints Penalized Q-learning for Safe Offline Reinforcement Learning—0
An Analysis of Reinforcement Learning for Malaria Control—0
Reward-Weighted Regression Converges to a Global OptimumCode0
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Benchmark Results

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