EnnaSachdeva
Research Engineer at Honda Research Institute
Honda Research InstituteSan Jose, California
Pinned Repositories
2R-manipulator-force-control
This is a MATLAB simulation for force control of a 2R manipulator using feedback linearization.
Algorithms
cvpr_dNRI
Code accompanying "Dynamic Neural Relational Inference" from CVPR 2020
D_VAE
MADyS
Code accompanying Multiagent Learning via Dynamic Skill Selection
Multiagent_ERL_heterogeneous_rover_domain
Non-holonomic-Trajectory-Planning-Using-the-Bernstein-Basis-Functions
Recurrent-Multiagent-Deep-Deterministic-Policy-Gradient-with-Difference-Rewards
Deep Reinforcement Learning (DRL) algorithms have been successfully applied to a range of challenging simulated continuous control single agent tasks. These methods have further been extended to multiagent domains in cooperative, competitive or mixed environments. This paper primarily focuses on multiagent cooperative settings which can be modeled for several real world problems such as coordination of autonomous vehicles and warehouse robots. However, these systems suffer from several challenges such as, structural credit assignment and partial observability. In this paper, we propose Recurrent Multiagent Deep Deterministic Policy Gradient (RMADDPG) algorithm which extends Multiagent Deep Determinisitic Policy Gradient algorithm - MADDPG \cite{lowe2017multi} by using a recurrent neural network for the actor policy. This helps to address partial observability by maintaining a sequence of past observations which networks learn to preserve in order to solve the POMDP. In addition, we use reward shaping through difference rewards to address structural credit assignment in a partially observed environment. We evaluate the performance of MADDPG and R-MADDPG with and without reward shaping in a Multiagent Particle Environment. We further show that reward shaped RMADDPG outperforms the baseline algorithm MADDPG in a partially observable environmental setting.
Resume-Screener
A project for CodeDay Labs that screens resumes based on their fit for a Software Engineer New Grad position
Robotics_Informative-Path-Planning
EnnaSachdeva's Repositories
EnnaSachdeva/Recurrent-Multiagent-Deep-Deterministic-Policy-Gradient-with-Difference-Rewards
Deep Reinforcement Learning (DRL) algorithms have been successfully applied to a range of challenging simulated continuous control single agent tasks. These methods have further been extended to multiagent domains in cooperative, competitive or mixed environments. This paper primarily focuses on multiagent cooperative settings which can be modeled for several real world problems such as coordination of autonomous vehicles and warehouse robots. However, these systems suffer from several challenges such as, structural credit assignment and partial observability. In this paper, we propose Recurrent Multiagent Deep Deterministic Policy Gradient (RMADDPG) algorithm which extends Multiagent Deep Determinisitic Policy Gradient algorithm - MADDPG \cite{lowe2017multi} by using a recurrent neural network for the actor policy. This helps to address partial observability by maintaining a sequence of past observations which networks learn to preserve in order to solve the POMDP. In addition, we use reward shaping through difference rewards to address structural credit assignment in a partially observed environment. We evaluate the performance of MADDPG and R-MADDPG with and without reward shaping in a Multiagent Particle Environment. We further show that reward shaped RMADDPG outperforms the baseline algorithm MADDPG in a partially observable environmental setting.
EnnaSachdeva/MADyS
Code accompanying Multiagent Learning via Dynamic Skill Selection
EnnaSachdeva/Non-holonomic-Trajectory-Planning-Using-the-Bernstein-Basis-Functions
EnnaSachdeva/Resume-Screener
A project for CodeDay Labs that screens resumes based on their fit for a Software Engineer New Grad position
EnnaSachdeva/Robotics_Informative-Path-Planning
EnnaSachdeva/Algorithms
EnnaSachdeva/cvpr_dNRI
Code accompanying "Dynamic Neural Relational Inference" from CVPR 2020
EnnaSachdeva/D_VAE
EnnaSachdeva/Multiagent_ERL_heterogeneous_rover_domain
EnnaSachdeva/CIFAR-10-binary-classification
EnnaSachdeva/Computer-Vision-Chroma-Keying
EnnaSachdeva/convert_kitti_to_ros
A useful ROS tool for dealing with KITTI point cloud dataset.
EnnaSachdeva/CppND-Route-Planning-Project
EnnaSachdeva/Deep-Learning-Implemetations
EnnaSachdeva/EKF-Localization
EnnaSachdeva/ennasachdeva.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
EnnaSachdeva/IROS-2017-COCrIP-Optimization
Optimization for estimating friction coefficient of materials to be used for In-Pipe climbing robot COCRIP (published in IROS-2017) for vertical and bend pipes.
EnnaSachdeva/Leisure_time_stuff
EnnaSachdeva/Machine_learning_algos
Machine learning algorithms
EnnaSachdeva/MAEDyS
EnnaSachdeva/Papers-Summary
This repository includes the summary of various papers in Robotics and AI, I have read.
EnnaSachdeva/releasing-research-code
Tips for releasing research code in Machine Learning (with official NeurIPS 2020 recommendations)
EnnaSachdeva/Robotics-Path-Planning
EnnaSachdeva/Rover-Domain
EnnaSachdeva/scenario_runner
Traffic scenario definition and execution engine
EnnaSachdeva/Scribe-Notes
Scribe notes on various topics relevant to robotics and AI.
EnnaSachdeva/subgoal-discovery
Learning from Trajectories via Subgoal Discovery
EnnaSachdeva/Trajectory-of-a-robot
EnnaSachdeva/Udacity_Computer_Vision_Nanodegree
EnnaSachdeva/vatic
Efficiently Scaling Up Video Annotation with Crowdsourced Marketplaces. IJCV 2012