saisurbehera Goto Github PK

abm-trafic-grid-commute

The Commute behavior, Traffic Congestion, and Parking model simulates traffic moving in a simplified city grid with several perpendicular one-way streets that forms an intersection. It allows you to control global variables, such as the parking ratio and the number of people, and explore traffic dynamics, particularly traffic congestion measured by average speed and average waiting time for parking, which add to the total utility. This model gives the cars destinations (house or work) to investigate the traffic flow of commuting people. The agents in this model use goal-based and adaptive cognition. ## HOW IT WORKS Each time step, the cars face the next destination they are trying to get to (work or house) and attempt to move forward at their current speed and keep a safety speed. If their current distance to the car directly in front of them is less than their safety distance, they decelerate. If the distance is larger than safety distance, they accelerate. If there is a red light or a stopped car in front of them, they stop. In each cycle, each car will be assigned one destination. Traffic lights at the intersection will automatically change at the beginning of each cycle. Once the car parked, it will take 20 seconds to get into the front door of a company and then the car dispears from the map, at the same time a parking spot is empty for the next one. If a car driving through its destination, however the parking spot is not empty, it will keep driving around until spot opens up.

annotated_deep_learning_paper_implementations

🧑‍🏫 50! Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

deep-learning-systems-performance

Assignments for COMS 6998 - Practical Deep Learning Systems Performance at Columbia

discussion1

dller_nlg

A project to understand temporal embeddings. This is work in progress

dreambooth-stable-diffusion

Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) with Stable Diffusion

dreamfusion_sd

Dreamfusion with Stable diffusion backend

eecs598-assignment

code for assignment for eecs598009

eecs598_assignment1

funsearch-llm

FunSearch with OpenAI

hackathon2021col

homework1-2021

This is my Homework 1

homework2-2021

hw2

homework3-2021

homework4-2021

Hw4 done with the first group

homework5-2021

hw5

homework6-2021

Homework6 2021 SI339

homework7-2021

homework7-2021

induction-subgoal-automata-rl

Implementation of "Induction of Subgoal Automata for Reinforcement Learning" (AAAI-20) and "Induction and Exploitation of Subgoal Automata for Reinforcement Learning" (JAIR).

k-bert

Source code of K-BERT

lean-gym

linear-attention-transformer

Transformer based on a variant of attention that is linear complexity in respect to sequence length

madewithml

Learn how to responsibly deliver value with ML.

mathproof

mathproof-

mathtokenizer

mengzi-retrieval-lm

An experimental implementation of the retrieval-enhanced language model

metaseq

Repo for external large-scale work

my_portfolio

nasa_air_quality_competition