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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 )

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Datasets

Latest papers with code

Robotic Surgery With Lean Reinforcement Learning

3 May 2021bluddy/lean_rl

As far as we know, this is the first time an RL-based agent is taught from visual data in a surgical robotics environment.

Q-LEARNING

2
03 May 2021

Action Candidate Based Clipped Double Q-learning for Discrete and Continuous Action Tasks

3 May 2021Jiang-HB/AC_CDQ

Finally, we use the maximum value in the second set of estimators to clip the action value of the chosen action in the first set of estimators and the clipped value is used for approximating the maximum expected action value.

Q-LEARNING

2
03 May 2021

Low-rank State-action Value-function Approximation

18 Apr 2021sergiorozada12/low-rank-rl

Value functions are central to Dynamic Programming and Reinforcement Learning but their exact estimation suffers from the curse of dimensionality, challenging the development of practical value-function (VF) estimation algorithms.

Q-LEARNING

0
18 Apr 2021

Optimal Market Making by Reinforcement Learning

8 Apr 2021mselser95/optimal-market-making

We apply Reinforcement Learning algorithms to solve the classic quantitative finance Market Making problem, in which an agent provides liquidity to the market by placing buy and sell orders while maximizing a utility function.

Q-LEARNING

0
08 Apr 2021

Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning

28 Mar 2021qdevpsi3/qrl-dqn-gym

We compare the performance of our model to that of a NN for agents that need similar time to convergence, and find that our quantum model needs approximately one-third of the parameters of the classical model to solve the Cart Pole environment in a similar number of episodes on average.

DECISION MAKING OPENAI GYM Q-LEARNING QUANTUM MACHINE LEARNING VISUAL QUESTION ANSWERING

6
28 Mar 2021

Balancing Rational and Other-Regarding Preferences in Cooperative-Competitive Environments

24 Feb 2021jbr-ai-labs/BAROCCO

Recent reinforcement learning studies extensively explore the interplay between cooperative and competitive behaviour in mixed environments.

MULTI-AGENT REINFORCEMENT LEARNING Q-LEARNING

1
24 Feb 2021

Understanding algorithmic collusion with experience replay

18 Feb 2021hanbingyan/collusion

In an infinitely repeated pricing game, pricing algorithms based on artificial intelligence (Q-learning) may consistently learn to charge supra-competitive prices even without communication.

Q-LEARNING

1
18 Feb 2021

Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19

9 Feb 2021exalearn/covid-drug-design

Design of new drug compounds with target properties is a key area of research in generative modeling.

Q-LEARNING

8
09 Feb 2021

Reinforcement Learning For Constraint Satisfaction Game Agents (15-Puzzle, Minesweeper, 2048, and Sudoku)

9 Feb 2021anavmehta/Reinforcement_Minesweeper

In recent years, reinforcement learning has seen interest because of deep Q-Learning, where the model is a convolutional neural network.

Q-LEARNING

1
09 Feb 2021

Revisiting Prioritized Experience Replay: A Value Perspective

5 Feb 2021RLforlife/VER

Furthermore, we successfully extend our theoretical framework to maximum-entropy RL by deriving the lower and upper bounds of these value metrics for soft Q-learning, which turn out to be the product of $|\text{TD}|$ and "on-policyness" of the experiences.

ATARI GAMES Q-LEARNING

1
05 Feb 2021