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 1826–1850 of 15113 papers

TitleStatusHype
First-Explore, then Exploit: Meta-Learning to Solve Hard Exploration-Exploitation Trade-OffsCode1
FlapAI Bird: Training an Agent to Play Flappy Bird Using Reinforcement Learning TechniquesCode1
Flexible Robust Beamforming for Multibeam Satellite Downlink using Reinforcement LearningCode1
Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on GraphsCode1
Flexible Attention-Based Multi-Policy Fusion for Efficient Deep Reinforcement LearningCode1
Rethinking the Implementation Matters in Cooperative Multi-Agent Reinforcement LearningCode1
HyperNCA: Growing Developmental Networks with Neural Cellular AutomataCode1
ICU-Sepsis: A Benchmark MDP Built from Real Medical DataCode1
ImagineBench: Evaluating Reinforcement Learning with Large Language Model RolloutsCode1
Forgetful Experience Replay in Hierarchical Reinforcement Learning from DemonstrationsCode1
Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPOCode1
FORK: A Forward-Looking Actor For Model-Free Reinforcement LearningCode1
For SALE: State-Action Representation Learning for Deep Reinforcement LearningCode1
Distributional Soft Actor-Critic: Off-Policy Reinforcement Learning for Addressing Value Estimation ErrorsCode1
RESPECT: Reinforcement Learning based Edge Scheduling on Pipelined Coral Edge TPUsCode1
An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy SearchCode1
Rethinking Goal-conditioned Supervised Learning and Its Connection to Offline RLCode1
From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster trainingCode1
Rethinking Value Function Learning for Generalization in Reinforcement LearningCode1
Hybrid Inverse Reinforcement LearningCode1
Benchmarking Actor-Critic Deep Reinforcement Learning Algorithms for Robotics Control with Action ConstraintsCode1
Hybrid intelligence for dynamic job-shop scheduling with deep reinforcement learning and attention mechanismCode1
AMAGO: Scalable In-Context Reinforcement Learning for Adaptive AgentsCode1
From Scratch to Sketch: Deep Decoupled Hierarchical Reinforcement Learning for Robotic Sketching AgentCode1
Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand SystemsCode1
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Benchmark Results

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