Praveen Palanisamy

multiple-object-tracking-lidar. C++ implementation to Detect, track and classify multiple objects using LIDAR scans or point cloud

macad-gym. Multi-Agent Connected Autonomous Driving (MACAD) Gym environments for Deep RL. Code for the paper presented in the Machine Learning for Autonomous Driving Workshop at NeurIPS 2019:

Ape-X-DQN. PyTorch Implementation of Ape-X (Distributed prioritized experience replay) architecture with DQN learner

webgym. WebGym: Web-browser-based tasks for RL Agents

macad-agents. Agents code for Multi-Agent Connected Autonomous Driving (MACAD) described in the paper presented in the Machine Learning for Autonomous Driving Workshop at NeurIPS 2019:

MemReFinder. Finder (File Explorer) App to chat with your Documents and Files powered by LLMs

OpenCV-2.4.9-for-arm. Prebuilt OpenCV library files for arm v7 cortex-a9 based devices including Pandaboard,I.mx6,ODROID-X

Robinhood-Insights. A web app to get actionable insights into your Robinhood Portfolio

Pandaboard-ES. Resources/How-TOs for OMAP4 based Pandaboard ES

playwrightgym. PlaywrightGym - Train RL Agents for Web tasks

multi_object_tracking_lidar-release. Release version of multi_object_tracking_lidar ROS package for: Multiple objects detection, tracking and classification from LIDAR scans/point-clouds

Hands-On-Intelligent-Agents-with-OpenAI-Gym. Code for Hands On Intelligent Agents with OpenAI Gym book to get started and learn to build deep reinforcement learning agents using PyTorch

openai-retro-contest. Code & experiments done for the transfer learning contest that measures a RL algorithm's ability to generalize from previous experience

web-llm. Bringing large-language models and chat to web browsers. Everything runs inside the browser with no server support.

autodemo. Demos as code — generate demo videos, interactive walkthroughs & marketing captures from your running web app. CI-native, agent-ready (MCP)