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

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Showing 3101–3125 of 15113 papers

TitleStatusHype
Human-Level Control without Server-Grade HardwareCode0
Human level control through deep reinforcement learningCode0
Reward Shaping for Human Learning via Inverse Reinforcement LearningCode0
Constrained Reinforcement Learning for Safe Heat Pump ControlCode0
Human-guided Robot Behavior Learning: A GAN-assisted Preference-based Reinforcement Learning ApproachCode0
Cross-domain Random Pre-training with Prototypes for Reinforcement LearningCode0
Human-Inspired Framework to Accelerate Reinforcement LearningCode0
Weak Human Preference Supervision For Deep Reinforcement LearningCode0
Hype or Heuristic? Quantum Reinforcement Learning for Join Order OptimisationCode0
Constrained Reinforcement Learning for Dexterous ManipulationCode0
How to Make Deep RL Work in PracticeCode0
Learning Transferable Reward for Query Object Localization with Policy AdaptationCode0
How to Build User Simulators to Train RL-based Dialog SystemsCode0
How to pick the domain randomization parameters for sim-to-real transfer of reinforcement learning policies?Code0
Constrained Policy OptimizationCode0
How Private Is Your RL Policy? An Inverse RL Based Analysis FrameworkCode0
How Helpful is Inverse Reinforcement Learning for Table-to-Text Generation?Code0
Multi-task Learning and Catastrophic Forgetting in Continual Reinforcement LearningCode0
How Many Random Seeds? Statistical Power Analysis in Deep Reinforcement Learning ExperimentsCode0
How RL Agents Behave When Their Actions Are ModifiedCode0
How to Sense the World: Leveraging Hierarchy in Multimodal Perception for Robust Reinforcement Learning AgentsCode0
Cross-View Policy Learning for Street NavigationCode0
Constrained Exploration and Recovery from Experience ShapingCode0
Least-Squares Policy IterationCode0
How Are Learned Perception-Based Controllers Impacted by the Limits of Robust Control?Code0
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Benchmark Results

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