Riashat/Deep-Bayesian-Active-Learning

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Created over 7 years ago
Updated almost 7 years ago

Riashat/Deep-Bayesian-Active-Learning

Riashat

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Code for Deep Bayesian Active Learning (ICML 2017)

Deep-Bayesian-Active-Learning

If you use this code for academic research, you are highly encouraged to cite the following paper: Yarin Gal, Riashat Islam, Zoubin Ghahramani. "Deep Bayesian Active Learning". ICML 2017. https://arxiv.org/pdf/1703.02910.pdf

Comments: Code available with both Keras and Lasagne. If you are using Keras, make sure you use the local keras version used in this repository.

Active-Learning-Bayesian-Convolutional-Neural-Networks

Active Learning on Image Data using Bayesian ConvNets

Q-Learning-SARSA-Policy-and-Value-Iteration

Implementation of basic reinforcement learning algorithms (Q-learning, SARSA, Policy iteration and Value Iteration) on benchmark RL MDPs (GridWorld, SmallWorld and CliffWorld)

Policy-Gradient-Reinforcement-Learning

Bayesian-Exploration-Deep-RL

Bayesian Uncertainty Exploration in Deep Reinforcement Learning

Temporal_Difference_Learning

Code for TD Learning algorithms in Reinforcement Learning

Dropout-Uncertainty-Exploration-DQN

Exploration Exploitation in DQNs using Dropout as Bayesian Approximation

meta-active-learning

Meta-Learning Bayesian Active Learning Algorithms

Model-Free-Control-Reinforcement-Learning

Multi-Step-Bootstrapping

Q(sigma), Expected SARSA and n-step Tree Backup Algorithms

Q_Sigma_Tree_Backup_Eligibility_Traces

Credit_Assignment_RL

Credit Assignment in Reinforcement Learning

Trust_DDPG

Trust Region constrained updates for DDPG

Plotting_Scripts

Sample Plotting Script to use

Soft_Trust_Region_A2C

Soft Trust Region Constraints on A2C