Exploration-Exploitation Dilemma Solving Methods

Overview

Exploration-Exploitation Dilemma Solving Methods

Medium article for this repo - HERE

In ths repo I implemented two techniques for tackling mentioned tradeoff. Methods Include:-

  • Epsilon Greedy (With different epsilons)
  • Thompson Sampling(also known as posterior sampling)

The reason for choosing these two only is to show the upper and lower bounds as epsilons are a starting point in dealing with these tradeoffs and Thompson Sampling is considered a recent state of the Art in this field.

ENV SPECIFICATIONS - A 10 arm testbed is simulated as same demonstrated in Sutton-Barto Book.
True Reward distribution (Here Action-2 is best)

Comparison Greedy(or Epsilon Greedies and TS

we used three different epsilons here for testing i.e:

  • epsilon = 0 => Greedy Agent
  • epsilon = 0.01 => exploration with 1% probability
  • epsilon = 0.1 => exploration with 10% probability

and TS

Averaged Over 2500 independent runs with 1500 timesteps

Comparison


Percentage Actions selected for epsilon = 0.01 and TS

Conclusion -> epsilon = 0.01 can be considered best for eps-greedies as it is increasing but pretty slow and the percentage Optimal Actions for it is Around 80% in later stages, on the other hand Thomsan Sampling shows a significant improvement in these results as it quickly explores and then exploit the optimal one with percentage goes upto almost 100 even very early!!.

In case you want to know more about TS visit this Reference.

Owner
Aman Mishra
B.Tech @ IIT BHU | Core interest in Digital Electronics, Statistics, Probability and Vector Calculus + their applications in Deep Learning and Robotics
Aman Mishra
PIXIE: Collaborative Regression of Expressive Bodies

PIXIE: Collaborative Regression of Expressive Bodies [Project Page] This is the official Pytorch implementation of PIXIE. PIXIE reconstructs an expres

Yao Feng 331 Jan 04, 2023
Xi Dongbo 78 Nov 29, 2022
Python3 / PyTorch implementation of the following paper: Fine-grained Semantics-aware Representation Enhancement for Self-supervisedMonocular Depth Estimation. ICCV 2021 (oral)

FSRE-Depth This is a Python3 / PyTorch implementation of FSRE-Depth, as described in the following paper: Fine-grained Semantics-aware Representation

77 Dec 28, 2022
Learning-based agent for Google Research Football

TiKick 1.Introduction Learning-based agent for Google Research Football Code accompanying the paper "TiKick: Towards Playing Multi-agent Football Full

Tsinghua AI Research Team for Reinforcement Learning 90 Dec 26, 2022
The DL Streamer Pipeline Zoo is a catalog of optimized media and media analytics pipelines.

The DL Streamer Pipeline Zoo is a catalog of optimized media and media analytics pipelines. It includes tools for downloading pipelines and their dependencies and tools for measuring their performace

8 Dec 04, 2022
SEAN: Image Synthesis with Semantic Region-Adaptive Normalization (CVPR 2020, Oral)

SEAN: Image Synthesis with Semantic Region-Adaptive Normalization (CVPR 2020 Oral) Figure: Face image editing controlled via style images and segmenta

Peihao Zhu 579 Dec 30, 2022
MPI-IS Mesh Processing Library

Perceiving Systems Mesh Package This package contains core functions for manipulating meshes and visualizing them. It requires Python 3.5+ and is supp

Max Planck Institute for Intelligent Systems 494 Jan 06, 2023
FCOSR: A Simple Anchor-free Rotated Detector for Aerial Object Detection

FCOSR: A Simple Anchor-free Rotated Detector for Aerial Object Detection FCOSR: A Simple Anchor-free Rotated Detector for Aerial Object Detection arXi

59 Nov 29, 2022
Kaggle Ultrasound Nerve Segmentation competition [Keras]

Ultrasound nerve segmentation using Keras (1.0.7) Kaggle Ultrasound Nerve Segmentation competition [Keras] #Install (Ubuntu {14,16}, GPU) cuDNN requir

179 Dec 28, 2022
Hso-groupie - A pwnable challenge in Real World CTF 4th

Hso-groupie - A pwnable challenge in Real World CTF 4th

Riatre Foo 42 Dec 05, 2022
Out-of-Town Recommendation with Travel Intention Modeling (AAAI2021)

TrainOR_AAAI21 This is the official implementation of our AAAI'21 paper: Haoran Xin, Xinjiang Lu, Tong Xu, Hao Liu, Jingjing Gu, Dejing Dou, Hui Xiong

Jack Xin 13 Oct 19, 2022
Manifold Alignment for Semantically Aligned Style Transfer

Manifold Alignment for Semantically Aligned Style Transfer [Paper] Getting Started MAST has been tested on CentOS 7.6 with python = 3.6. It supports

35 Nov 14, 2022
Toward Realistic Single-View 3D Object Reconstruction with Unsupervised Learning from Multiple Images (ICCV 2021)

Table of Content Introduction Getting Started Datasets Installation Experiments Training & Testing Pretrained models Texture fine-tuning Demo Toward R

VinAI Research 42 Dec 05, 2022
A simple code to convert image format and channel as well as resizing and renaming multiple images.

Rename-Resize-and-convert-multiple-images A simple code to convert image format and channel as well as resizing and renaming multiple images. This cod

Happy N. Monday 3 Feb 15, 2022
Implementation of SwinTransformerV2 in TensorFlow.

SwinTransformerV2-TensorFlow A TensorFlow implementation of SwinTransformerV2 by Microsoft Research Asia, based on their official implementation of Sw

Phan Nguyen 2 May 30, 2022
Cluttered MNIST Dataset

Cluttered MNIST Dataset A setup script will download MNIST and produce mnist/*.t7 files: luajit download_mnist.lua Example usage: local mnist_clutter

DeepMind 50 Jul 12, 2022
Embeds a story into a music playlist by sorting the playlist so that the order of the music follows a narrative arc.

playlist-story-builder This project attempts to embed a story into a music playlist by sorting the playlist so that the order of the music follows a n

Dylan R. Ashley 0 Oct 28, 2021
Author's PyTorch implementation of TD3+BC, a simple variant of TD3 for offline RL

A Minimalist Approach to Offline Reinforcement Learning TD3+BC is a simple approach to offline RL where only two changes are made to TD3: (1) a weight

Scott Fujimoto 193 Dec 23, 2022
BDDM: Bilateral Denoising Diffusion Models for Fast and High-Quality Speech Synthesis

Bilateral Denoising Diffusion Models (BDDMs) This is the official PyTorch implementation of the following paper: BDDM: BILATERAL DENOISING DIFFUSION M

172 Dec 23, 2022
Bounding Wasserstein distance with couplings

BoundWasserstein These scripts reproduce the results of the article Bounding Wasserstein distance with couplings by Niloy Biswas and Lester Mackey. ar

Niloy Biswas 1 Jan 11, 2022