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 8476–8500 of 15113 papers

TitleStatusHype
LASER: Learning a Latent Action Space for Efficient Reinforcement Learning—0
Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark—0
Robust Reinforcement Learning under model misspecificationCode0
ReAgent: Point Cloud Registration using Imitation and Reinforcement LearningCode1
Self-adaptive Torque Vectoring Controller Using Reinforcement LearningCode0
KnowRU: Knowledge Reusing via Knowledge Distillation in Multi-agent Reinforcement Learning—0
Autonomous Overtaking in Gran Turismo Sport Using Curriculum Reinforcement Learning—0
Increasing the Efficiency of Policy Learning for Autonomous Vehicles by Multi-Task Representation Learning—0
A Convex Programming Approach to Data-Driven Risk-Averse Reinforcement Learning—0
Agent with Warm Start and Adaptive Dynamic Termination for Plane Localization in 3D UltrasoundCode1
Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlowCode1
Barrier Function-based Safe Reinforcement Learning for Emergency Control of Power Systems—0
Character Controllers Using Motion VAEsCode1
Model-Free Learning of Safe yet Effective Controllers—0
Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots—0
MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement LearningCode1
Nearly Horizon-Free Offline Reinforcement Learning—0
Reinforcement Learning for Deceiving Reactive Jammers in Wireless Networks—0
AoA-Based Pilot Assignment in Massive MIMO Systems Using Deep Reinforcement Learning—0
A Meta-Reinforcement Learning Approach to Process Control—0
Improving Playtesting Coverage via Curiosity Driven Reinforcement Learning Agents—0
Hierarchical Program-Triggered Reinforcement Learning Agents For Automated Driving—0
Self-Imitation Learning by Planning—0
Model Predictive Actor-Critic: Accelerating Robot Skill Acquisition with Deep Reinforcement LearningCode1
Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning—0
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Benchmark Results

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