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  • āœØ Iā€™m currently developing algorithm to facilitate robot learning and safe autonomy.
  • šŸŽ“ You can find my current research interests here
  • :trollface: Ping me up if you want to talk about robots, life, music, movies and memes


My Skill Set

Programming Languages

C++ python tf pytorch jax react node sql html css

Softwares and Utilities

ros gazebo rviz docker matlab simulink opensim solidworks ansys

Others

Linux Git Bash latex

Stats


Utkarsh Mishra's Projects

algorithmscpp icon algorithmscpp

This includes the various algorithms practised by me , that are used in competitive and other developement programmings.

bioimitation-gym icon bioimitation-gym

An OpenAI-Gym Package for Training and Testing Reinforcement Learning algorithms with OpenSim Models

d4rl icon d4rl

A benchmark for offline reinforcement learning.

deeptraffic icon deeptraffic

DeepTraffic is a deep reinforcement learning competition, part of the MIT Deep Learning series.

dmd-mpc-rl icon dmd-mpc-rl

This repository contains the code for our paper on Dynamic Mirror Descent based MPC for Model-Free RL

drawio icon drawio

draw.io is a JavaScript, client-side editor for general diagramming and whiteboarding

jaxrl icon jaxrl

Jax (Flax) implementation of algorithms for Deep Reinforcement Learning with continuous action spaces.

machine-learning-projects icon machine-learning-projects

The repository contains various Machine Learning based solutions for data analysis, regression and clustering problems

openaistuffs icon openaistuffs

Reinforcement Learning: The repository contains various DDPG and DQN variants of OpenAI environments

osiml2m2019envddpg icon osiml2m2019envddpg

This is my RL-DDPG implementation of osim-rl L2M2019 Environment (Work Still Under progress)

pisac icon pisac

Tensorflow source code for the PI-SAC agent from "Predictive Information Accelerates Learning in RL" (NeurIPS 2020)

pixel-representations-rl icon pixel-representations-rl

This repository is a collection of widely used self-supervised auxiliary losses used for learning representations in reinforcement learning.

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