Simulating an AI playing 2048 using the Expectimax algorithm

Overview

2048-expectimax

Simulating an AI playing 2048 using the Expectimax algorithm

The base game engine uses code from here.

The AI player is modeled as a max player, and the computer as a chance player (picking a random open spot to place a 2-tile). The score returned by the game engine is used as the evaluation function value at the leaf nodes of the trees.

You can play the game manually using the arrow keys. Pressing 'Enter' will let the AI play, and pressing 'Enter' again will stop the AI player. Read the game engine code from 'game.py' and see how it returns the game state and evaluate its score from an arbitrary game state after an arbitrary player move.

A depth-3 game tree means the tree should have the following levels:

  • root: player
  • level 1: computer
  • level 2: player
  • level 3: terminal with payoff (note that we say "terminal" to mean the leaf nodes in the shallow game tree, not the termination of the game itself)

This tree represents all the game states of a player-computer-player sequence (the player makes a move, the computer place a tile, and then the player makes another move, and then evaluate the score) from the current state.

Usage

To run the program:

    python main.py

Once your program is running, here are a few keyboard options available in-game:

  • 'r': restart the game
  • 'u': undo a move
  • '3'-'7': change board size
  • 'g': toggle grayscale
Owner
Subha Ramesh
Subha Ramesh
existing and custom freqtrade strategies supporting the new hyperstrategy format.

freqtrade-strategies Description Existing and self-developed strategies, rewritten to support the new HyperStrategy format from the freqtrade-develop

39 Aug 20, 2021
Code base for reproducing results of I.Schubert, D.Driess, O.Oguz, and M.Toussaint: Learning to Execute: Efficient Learning of Universal Plan-Conditioned Policies in Robotics. NeurIPS (2021)

Learning to Execute (L2E) Official code base for completely reproducing all results reported in I.Schubert, D.Driess, O.Oguz, and M.Toussaint: Learnin

3 May 18, 2022
SiT: Self-supervised vIsion Transformer

This repository contains the official PyTorch self-supervised pretraining, finetuning, and evaluation codes for SiT (Self-supervised image Transformer).

Sara Ahmed 275 Dec 28, 2022
This is the codebase for Diffusion Models Beat GANS on Image Synthesis.

This is the codebase for Diffusion Models Beat GANS on Image Synthesis.

OpenAI 3k Dec 26, 2022
Deep motion transfer

animation-with-keypoint-mask Paper The right most square is the final result. Softmax mask (circles): \ Heatmap mask: \ conda env create -f environmen

9 Nov 01, 2022
Two-Stage Peer-Regularized Feature Recombination for Arbitrary Image Style Transfer

Two-Stage Peer-Regularized Feature Recombination for Arbitrary Image Style Transfer Paper on arXiv Public PyTorch implementation of two-stage peer-reg

NNAISENSE 38 Oct 14, 2022
Jiminy Cricket Environment (NeurIPS 2021)

Jiminy Cricket This is the repository for "What Would Jiminy Cricket Do? Towards Agents That Behave Morally" by Dan Hendrycks*, Mantas Mazeika*, Andy

Dan Hendrycks 15 Aug 29, 2022
OpenAi's gym environment wrapper to vectorize them with Ray

Ray Vector Environment Wrapper You would like to use Ray to vectorize your environment but you don't want to use RLLib ? You came to the right place !

Pierre TASSEL 15 Nov 10, 2022
NaturalCC is a sequence modeling toolkit that allows researchers and developers to train custom models

NaturalCC NaturalCC is a sequence modeling toolkit that allows researchers and developers to train custom models for many software engineering tasks,

159 Dec 28, 2022
Regulatory Instruments for Fair Personalized Pricing.

Fair pricing Source code for WWW 2022 paper Regulatory Instruments for Fair Personalized Pricing. Installation Requirements Linux with Python = 3.6 p

Renzhe Xu 6 Oct 26, 2022
Evaluating saliency methods on artificial data with different background types

Evaluating saliency methods on artificial data with different background types This repository contains the relevant code for the MedNeurips 2021 subm

2 Jul 05, 2022
Official code for "InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization" (ICLR 2020, spotlight)

InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization Authors: Fan-yun Sun, Jordan Hoffm

Fan-Yun Sun 232 Dec 28, 2022
VSR-Transformer - This paper proposes a new Transformer for video super-resolution (called VSR-Transformer).

VSR-Transformer By Jiezhang Cao, Yawei Li, Kai Zhang, Luc Van Gool This paper proposes a new Transformer for video super-resolution (called VSR-Transf

Jiezhang Cao 225 Nov 13, 2022
PyTorch code for ICPR 2020 paper Future Urban Scene Generation Through Vehicle Synthesis

Future urban scene generation through vehicle synthesis This repository contains Pytorch code for the ICPR2020 paper "Future Urban Scene Generation Th

Alessandro Simoni 4 Oct 11, 2021
Official PyTorch implementation of MAAD: A Model and Dataset for Attended Awareness

MAAD: A Model for Attended Awareness in Driving Install // Datasets // Training // Experiments // Analysis // License Official PyTorch implementation

7 Oct 16, 2022
Official code base for the poster "On the use of Cortical Magnification and Saccades as Biological Proxies for Data Augmentation" published in NeurIPS 2021 Workshop (SVRHM)

Self-Supervised Learning (SimCLR) with Biological Plausible Image Augmentations Official code base for the poster "On the use of Cortical Magnificatio

Binxu 8 Aug 17, 2022
Codes and scripts for "Explainable Semantic Space by Grounding Languageto Vision with Cross-Modal Contrastive Learning"

Visually Grounded Bert Language Model This repository is the official implementation of Explainable Semantic Space by Grounding Language to Vision wit

17 Dec 17, 2022
LineBoard - Python+React+MySQL-白板即時系統改善人群行為

LineBoard-白板即時系統改善人群行為 即時顯示實驗室的使用狀況,並遠端預約排隊,以此來改善人們的工作效率 程式架構 運作流程 使用者先至該實驗室網站預約

Bo-Jyun Huang 1 Feb 22, 2022
[ICLR 2022] DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR

DAB-DETR This is the official pytorch implementation of our ICLR 2022 paper DAB-DETR. Authors: Shilong Liu, Feng Li, Hao Zhang, Xiao Yang, Xianbiao Qi

336 Dec 25, 2022
Exact Pareto Optimal solutions for preference based Multi-Objective Optimization

Exact Pareto Optimal solutions for preference based Multi-Objective Optimization

Debabrata Mahapatra 40 Dec 24, 2022