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 776–800 of 15113 papers

TitleStatusHype
Agents that Listen: High-Throughput Reinforcement Learning with Multiple Sensory SystemsCode1
Agent-Temporal Attention for Reward Redistribution in Episodic Multi-Agent Reinforcement LearningCode1
Bridging the Gap Between f-GANs and Wasserstein GANsCode1
Bayesian Soft Actor-Critic: A Directed Acyclic Strategy Graph Based Deep Reinforcement LearningCode1
Agent with Warm Start and Active Termination for Plane Localization in 3D UltrasoundCode1
Agent with Warm Start and Adaptive Dynamic Termination for Plane Localization in 3D UltrasoundCode1
Building a 3-Player Mahjong AI using Deep Reinforcement LearningCode1
Demonstration-Guided Reinforcement Learning with Learned SkillsCode1
Asynchronous Reinforcement Learning for Real-Time Control of Physical RobotsCode1
Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-TuningCode1
CaiRL: A High-Performance Reinforcement Learning Environment ToolkitCode1
Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?Code1
Eigenoption Discovery through the Deep Successor RepresentationCode1
Delay-Aware Model-Based Reinforcement Learning for Continuous ControlCode1
Delay-Aware Multi-Agent Reinforcement Learning for Cooperative and Competitive EnvironmentsCode1
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?Code1
CARL: A Benchmark for Contextual and Adaptive Reinforcement LearningCode1
Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative ExplorationCode1
Can Wikipedia Help Offline Reinforcement Learning?Code1
Active Inference for Stochastic ControlCode1
CARL: Controllable Agent with Reinforcement Learning for Quadruped LocomotionCode1
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority InfluenceCode1
Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous InferenceCode1
Denoised MDPs: Learning World Models Better Than the World ItselfCode1
Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV with Thrust Vectoring RotorsCode1
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Benchmark Results

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