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Q-Learning

The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances.

( Image credit: Playing Atari with Deep Reinforcement Learning )

Papers

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Showing 1431–1440 of 1918 papers

TitleStatusHype
ViZDoom: DRQN with Prioritized Experience Replay, Double-Q Learning, & Snapshot Ensembling—0
V-Learning -- A Simple, Efficient, Decentralized Algorithm for Multiagent RL—0
VLM Q-Learning: Aligning Vision-Language Models for Interactive Decision-Making—0
VOQL: Towards Optimal Regret in Model-free RL with Nonlinear Function Approximation—0
Wasserstein Actor-Critic: Directed Exploration via Optimism for Continuous-Actions Control—0
Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog—0
Way Off-Policy Batch Deep Reinforcement Learning of Human Preferences in Dialog—0
Weakly Coupled Deep Q-Networks—0
Weighted Bellman Backups for Improved Signal-to-Noise in Q-Updates—0
Weighted Double Deep Multiagent Reinforcement Learning in Stochastic Cooperative Environments—0
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