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 10051–10075 of 15113 papers

TitleStatusHype
GRIMGEP: Learning Progress for Robust Goal Sampling in Visual Deep Reinforcement Learning—0
Deep Reinforcement Learning with Label Embedding Reward for Supervised Image Hashing—0
Hierarchical Reinforcement Learning in StarCraft II with Human Expertise in Subgoals Selection—0
TriFinger: An Open-Source Robot for Learning DexterityCode1
Managing caching strategies for stream reasoning with reinforcement learning—0
SafePILCO: a software tool for safe and data-efficient policy synthesisCode1
Physics-Based Dexterous Manipulations with Estimated Hand Poses and Residual Reinforcement Learning—0
Towards Sample Efficient Agents through Algorithmic AlignmentCode0
Distributed Deep Reinforcement Learning for Functional Split Control in Energy Harvesting Virtualized Small Cells—0
Incremental Text to Speech for Neural Sequence-to-Sequence Models using Reinforcement Learning—0
A Machine of Few Words -- Interactive Speaker Recognition with Reinforcement Learning—0
Adaptive Coordination Offsets for Signalized Arterial Intersections using Deep Reinforcement Learning—0
Towards General and Autonomous Learning of Core Skills: A Case Study in Locomotion—0
The Emergence of Adversarial Communication in Multi-Agent Reinforcement LearningCode1
A Gentle Lecture Note on Filtrations in Reinforcement Learning—0
Contrastive Variational Reinforcement Learning for Complex ObservationsCode1
Deep Q-Network Based Multi-agent Reinforcement Learning with Binary Action Agents—0
Deep Reinforcement Learning for Tactile Robotics: Learning to Type on a Braille KeyboardCode0
Fashion Captioning: Towards Generating Accurate Descriptions with Semantic RewardsCode1
Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without SacrificesCode1
Deep reinforcement learning to detect brain lesions on MRI: a proof-of-concept application of reinforcement learning to medical images—0
Mixed-Initiative Level Design with RL BrushCode0
Deep Reinforcement Learning for Field Development Optimization—0
Learning Power Control from a Fixed Batch of Data—0
Reinforcement Learning-driven Information Seeking: A Quantum Probabilistic Approach—0
Show:102550
← PrevPage 403 of 605Next →

Benchmark Results

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