Kaustubh Mani's repositories
monolayout
MonoLayout: Amodal Scene Layout from a single image
papers_trove
Collection of papers and corresponding notes relevant to my research.
ai-safety-gridworlds
This is a suite of reinforcement learning environments illustrating various safety properties of intelligent agents.
Bullet-Safety-Gym
An open-source framework to benchmark and assess safety specifications of Reinforcement Learning problems.
cleanrl
High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
deep-learning-uncertainty
Literature survey, paper reviews, experimental setups and a collection of implementations for baselines methods for predictive uncertainty estimation in deep learning models.
DEUP
Code for experiments to learn uncertainty
e3b
Official repo for the E3B algorithm described in the paper "Exploration via Elliptical Episodic Bonuses".
exploring_exploration
This paper contains code for our work "An Exploration of Embodied Visual Exploration".
FSRL
🚀 A fast safe reinforcement learning library in PyTorch
Gymnasium
An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)
Gymnasium-Robotics
A collection of robotics simulation environments for reinforcement learning
habitat-lab
A modular high-level library to train embodied AI agents across a variety of tasks, environments, and simulators.
habitat_envs
Gym environments for habitat
IsaacGymEnvs
Isaac Gym Reinforcement Learning Environments
metadrive_clean
MetaDrive-0.2.6.0 that is compatible with newest gym version.
old_website
My personal webpage
omnisafe
OmniSafe is an infrastructural framework for accelerating SafeRL research.
ReDMan
ReDMan is an open-source simulation platform that provides a standardized implementation of safe RL algorithms for Reliable Dexterous Manipulation.
rl-sandbox
Working codebase of RL algorithms (for use in future research)
RLeXplore
RLeXplore provides stable baselines of exploration methods in reinforcement learning, such as intrinsic curiosity module (ICM), random network distillation (RND) and rewarding impact-driven exploration (RIDE).
robo-gym
An open source toolkit for Distributed Deep Reinforcement Learning on real and simulated robots.
Safe-Policy-Optimization
NeurIPS 2023: Safe Policy Optimization: A benchmark repository for safe reinforcement learning algorithms
stable-baselines3
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.