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 9751–9775 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning with Distributional Semantic Rewards for Abstractive Summarization—0
Deep Reinforcement Learning With Adaptive Combined Critics—0
Deep Reinforcement Learning with Adjustments—0
Deep reinforcement learning with automated label extraction from clinical reports accurately classifies 3D MRI brain volumes—0
Deep Reinforcement Learning with Discrete Normalized Advantage Functions for Resource Management in Network Slicing—0
Deep Reinforcement Learning with Embedded LQR Controllers—0
Deep Reinforcement Learning with Explicit Context Representation—0
Spatio-Temporal Graph Convolutional Neural Networks for Physics-Aware Grid Learning Algorithms—0
Deep Reinforcement Learning with Hybrid Intrinsic Reward Model—0
Deep Reinforcement Learning with Implicit Human Feedback—0
Deep Reinforcement Learning with Interactive Feedback in a Human-Robot Environment—0
Deep Reinforcement Learning with Iterative Shift for Visual Tracking—0
Deep Reinforcement Learning with Label Embedding Reward for Supervised Image Hashing—0
Deep Reinforcement Learning with Linear Quadratic Regulator Regions—0
Deep Reinforcement Learning With Macro-Actions—0
Deep Reinforcement Learning with Mixed Convolutional Network—0
Deep Reinforcement Learning with Model Learning and Monte Carlo Tree Search in Minecraft—0
Deep Reinforcement Learning with Plasticity Injection—0
Deep Reinforcement Learning with Quantum-inspired Experience Replay—0
Deep reinforcement learning with relational inductive biases—0
Deep Reinforcement Learning with Shallow Controllers: An Experimental Application to PID Tuning—0
Deep Reinforcement Learning with Robust and Smooth Policy—0
Deep Reinforcement Learning with Smooth Policy—0
Deep Reinforcement Learning with Spiking Q-learning—0
Deep Reinforcement Learning with Successor Features for Navigation across Similar Environments—0
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Benchmark Results

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