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 8376–8400 of 15113 papers

TitleStatusHype
One-Shot Learning of Manipulation Skills with Online Dynamics Adaptation and Neural Network Priors—0
One-shot, Offline and Production-Scalable PID Optimisation with Deep Reinforcement Learning—0
One-Step Distributional Reinforcement Learning—0
Sample Complexity of Offline Reinforcement Learning with Deep ReLU Networks—0
On Gap-dependent Bounds for Offline Reinforcement Learning—0
On Generalization and Distributional Update for Mimicking Observations with Adequate Exploration—0
On Hard Exploration for Reinforcement Learning: a Case Study in Pommerman—0
On Improving Cross-dataset Generalization of Deepfake Detectors—0
On Improving Deep Reinforcement Learning for POMDPs—0
On Inductive Biases in Deep Reinforcement Learning—0
On Information Asymmetry in Competitive Multi-Agent Reinforcement Learning: Convergence and Optimality—0
On Instance-Dependent Bounds for Offline Reinforcement Learning with Linear Function Approximation—0
On Jointly Optimizing Partial Offloading and SFC Mapping: A Cooperative Dual-agent Deep Reinforcement Learning Approach—0
On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models—0
Online 3D Bin Packing Reinforcement Learning Solution with Buffer—0
Online Adaptation of Deep Architectures with Reinforcement Learning—0
Online Adaptive Optimal Control Algorithm Based on Synchronous Integral Reinforcement Learning With Explorations—0
Online Algorithms and Policies Using Adaptive and Machine Learning Approaches—0
Online Antenna Tuning in Heterogeneous Cellular Networks with Deep Reinforcement Learning—0
Online Attentive Kernel-Based Temporal Difference Learning—0
Online Behavior Modification for Expressive User Control of RL-Trained Robots—0
Online Bootstrap Inference For Policy Evaluation in Reinforcement Learning—0
On-line Building Energy Optimization using Deep Reinforcement Learning—0
Online Constrained Model-based Reinforcement Learning—0
Online Contrastive Divergence with Generative Replay: Experience Replay without Storing Data—0
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Benchmark Results

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