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 5226–5250 of 15113 papers

TitleStatusHype
Image Captioning based on Deep Reinforcement Learning—0
Image Deraining via Self-supervised Reinforcement Learning—0
Image-Guided Navigation of a Robotic Ultrasound Probe for Autonomous Spinal Sonography Using a Shadow-aware Dual-Agent Framework—0
Image quality assessment for machine learning tasks using meta-reinforcement learning—0
Image Synthesis for Data Augmentation in Medical CT using Deep Reinforcement Learning—0
Imagination-Augmented Hierarchical Reinforcement Learning for Safe and Interactive Autonomous Driving in Urban Environments—0
Imagined Value Gradients: Model-Based Policy Optimization with Transferable Latent Dynamics Models—0
Imagine Networks—0
Imitate then Transcend: Multi-Agent Optimal Execution with Dual-Window Denoise PPO—0
Imitating, Fast and Slow: Robust learning from demonstrations via decision-time planning—0
Imitating Opponent to Win: Adversarial Policy Imitation Learning in Two-player Competitive Games—0
Imitating Past Successes can be Very Suboptimal—0
SPOC: Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World—0
Imitation Bootstrapped Reinforcement Learning—0
Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios—0
Forward and inverse reinforcement learning sharing network weights and hyperparameters—0
Imitation Learning for Human Pose Prediction—0
Imitation Learning with Concurrent Actions in 3D Games—0
Imitation-Projected Programmatic Reinforcement Learning—0
Imitation with Neural Density Models—0
IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Making—0
Imminent Collision Mitigation with Reinforcement Learning and Vision—0
IMPACT: Importance Weighted Asynchronous Architectures with Clipped Target Networks—0
Impact of Price Inflation on Algorithmic Collusion Through Reinforcement Learning Agents—0
Impedance Matching: Enabling an RL-Based Running Jump in a Quadruped Robot—0
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Benchmark Results

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