Q-Learning
388 papers with code • 0 benchmarks • 2 datasets
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 )
Benchmarks
These leaderboards are used to track progress in Q-Learning
Libraries
Use these libraries to find Q-Learning models and implementations
VizDoom
Yeast
Most implemented papers
Designing Neural Network Architectures using Reinforcement Learning
We introduce MetaQNN, a meta-modeling algorithm based on reinforcement learning to automatically generate high-performing CNN architectures for a given learning task.
Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning
cts198859/deeprl_dist
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ICML 2017
Many real-world problems, such as network packet routing and urban traffic control, are naturally modeled as multi-agent reinforcement learning (RL) problems.
Deep Q-learning from Demonstrations
We present an algorithm, Deep Q-learning from Demonstrations (DQfD), that leverages small sets of demonstration data to massively accelerate the learning process even from relatively small amounts of demonstration data and is able to automatically assess the necessary ratio of demonstration data while learning thanks to a prioritized replay mechanism.
SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewards
opendilab/DI-engine
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ICLR 2020
Theoretically, we show that SQIL can be interpreted as a regularized variant of BC that uses a sparsity prior to encourage long-horizon imitation.
QPLEX: Duplex Dueling Multi-Agent Q-Learning
This paper presents a novel MARL approach, called duPLEX dueling multi-agent Q-learning (QPLEX), which takes a duplex dueling network architecture to factorize the joint value function.
IQ-Learn: Inverse soft-Q Learning for Imitation
Div99/IQ-Learn
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NeurIPS 2021
In many sequential decision-making problems (e. g., robotics control, game playing, sequential prediction), human or expert data is available containing useful information about the task.
Multiagent Cooperation and Competition with Deep Reinforcement Learning
NeuroCSUT/DeepMind-Atari-Deep-Q-Learner-2Player
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In the present work we extend the Deep Q-Learning Network architecture proposed by Google DeepMind to multiagent environments and investigate how two agents controlled by independent Deep Q-Networks interact in the classic videogame Pong.
Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning
andyzeng/visual-pushing-grasping
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Skilled robotic manipulation benefits from complex synergies between non-prehensile (e. g. pushing) and prehensile (e. g. grasping) actions: pushing can help rearrange cluttered objects to make space for arms and fingers; likewise, grasping can help displace objects to make pushing movements more precise and collision-free.
Benchmarking Batch Deep Reinforcement Learning Algorithms
sfujim/BCQ
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Widely-used deep reinforcement learning algorithms have been shown to fail in the batch setting--learning from a fixed data set without interaction with the environment.
Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
oxwhirl/wqmix
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NeurIPS 2020
We show in particular that this projection can fail to recover the optimal policy even with access to $Q^*$, which primarily stems from the equal weighting placed on each joint action.
