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

Showing 301350 of 15113 papers

TitleStatusHype
Demonstration-Guided Reinforcement Learning with Efficient Exploration for Task Automation of Surgical RobotCode2
Constrained Decision Transformer for Offline Safe Reinforcement LearningCode2
Grounding Large Language Models in Interactive Environments with Online Reinforcement LearningCode2
Efficient Online Reinforcement Learning with Offline DataCode2
Off-the-Grid MARL: Datasets with Baselines for Offline Multi-Agent Reinforcement LearningCode2
Heterogeneous Multi-Robot Reinforcement LearningCode2
Learning Physically Realizable Skills for Online Packing of General 3D ShapesCode2
MO-Gym: A Library of Multi-Objective Reinforcement Learning EnvironmentsCode2
Learning Heterogeneous Agent Cooperation via Multiagent League TrainingCode2
A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, ChallengesCode2
DIAMBRA Arena: a New Reinforcement Learning Platform for Research and ExperimentationCode2
Harfang3D Dog-Fight Sandbox: A Reinforcement Learning Research Platform for the Customized Control Tasks of Fighter AircraftsCode2
In-Hand Object Rotation via Rapid Motor AdaptationCode2
Pareto Actor-Critic for Equilibrium Selection in Multi-Agent Reinforcement LearningCode2
On Efficient Reinforcement Learning for Full-length Game of StarCraft IICode2
Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement LearningCode2
Transformers are Sample-Efficient World ModelsCode2
A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement LearningCode2
Diffusion Policies as an Expressive Policy Class for Offline Reinforcement LearningCode2
A Cooperation Graph Approach for Multiagent Sparse Reward Reinforcement LearningCode2
Deep Reinforcement Learning for Multi-Agent InteractionCode2
CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement LearningCode2
DayDreamer: World Models for Physical Robot LearningCode2
Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement LearningCode2
Challenges and Opportunities in Offline Reinforcement Learning from Visual ObservationsCode2
Neuro-Nav: A Library for Neurally-Plausible Reinforcement LearningCode2
Human-AI Shared Control via Policy DissectionCode2
Multi-Agent Reinforcement Learning is a Sequence Modeling ProblemCode2
DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal SystemsCode2
RLPrompt: Optimizing Discrete Text Prompts with Reinforcement LearningCode2
A Review of Safe Reinforcement Learning: Methods, Theory and ApplicationsCode2
CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement LearningCode2
Training a Helpful and Harmless Assistant with Reinforcement Learning from Human FeedbackCode2
JORLDY: a fully customizable open source framework for reinforcement learningCode2
VRL3: A Data-Driven Framework for Visual Deep Reinforcement LearningCode2
Online Decision TransformerCode2
Tutorial on amortized optimizationCode2
moolib: A Platform for Distributed RLCode2
Reinforcement Learning TextbookCode2
FinRL-Meta: A Universe of Near-Real Market Environments for Data-Driven Deep Reinforcement Learning in Quantitative FinanceCode2
ElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement LearningCode2
Godot Reinforcement Learning AgentsCode2
Policy improvement by planning with GumbelCode2
MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement LearningCode2
Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement LearningCode2
Physics-based Deep LearningCode2
The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement LearningCode2
A Comparative Study of Algorithms for Intelligent Traffic Signal ControlCode2
What Matters in Learning from Offline Human Demonstrations for Robot ManipulationCode2
Habitat 2.0: Training Home Assistants to Rearrange their HabitatCode2
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Benchmark Results

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