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 5551–5575 of 15113 papers

TitleStatusHype
Autotelic Agents with Intrinsically Motivated Goal-Conditioned Reinforcement Learning: a Short Survey—0
Intrinsically Motivated Learning of Causal World Models—0
Intrinsically Motivated Learning of Visual Motion Perception and Smooth Pursuit—0
Intrinsically motivated option learning: a comparative study of recent methods—0
Intrinsically-Motivated Reinforcement Learning: A Brief Introduction—0
Intrinsically Motivated Reinforcement Learning based Recommendation with Counterfactual Data Augmentation—0
Intrinsically Motivated Self-supervised Learning in Reinforcement Learning—0
Intrinsic Exploration as Multi-Objective RL—0
Intrinsic Motivation Driven Intuitive Physics Learning using Deep Reinforcement Learning with Intrinsic Reward Normalization—0
Intrinsic Motivation in Model-based Reinforcement Learning: A Brief Review—0
Intrinsic Rewards for Exploration without Harm from Observational Noise: A Simulation Study Based on the Free Energy Principle—0
Introducing Symmetries to Black Box Meta Reinforcement Learning—0
Introduction to Machine Learning for Accelerator Physics—0
Introduction to Machine Learning for the Sciences—0
Introduction to Quantum Reinforcement Learning: Theory and PennyLane-based Implementation—0
Introduction to Soar—0
Introduction to the "Industrial Benchmark"—0
Introspection-based Explainable Reinforcement Learning in Episodic and Non-episodic Scenarios—0
Introspection Learning—0
InTune: Reinforcement Learning-based Data Pipeline Optimization for Deep Recommendation Models—0
Invariant Representations for Reinforcement Learning without Reconstruction—0
Identifying Cognitive Radars -- Inverse Reinforcement Learning using Revealed Preferences—0
Inverse Design of Grating Couplers Using the Policy Gradient Method from Reinforcement Learning—0
Inverse Design of Photonic Crystal Surface Emitting Lasers is a Sequence Modeling Problem—0
Inverse-Inverse Reinforcement Learning. How to Hide Strategy from an Adversarial Inverse Reinforcement Learner—0
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Benchmark Results

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