AI Course Project 14032 - Dr. TanGhatari
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Updated
Sep 14, 2025
AI Course Project 14032 - Dr. TanGhatari
A simple calculator widget for SAP Analytics Cloud
PyTorch implementation of SAC (Soft Actor-Critic)
Reinforcement learning agents (TD3 & SAC) for AirHockey, exploring the impact of policy noise, delay, and other training variations.
PROJECT MIGRATED TO CODEBERG - Reinforcement Learning in Multiplicative Domains
Adversarial Patch defense using SegmentAndComplete (SAC) & Masked AutoEncoder (MAE)
Funções de Sistemas de Financiamento comparação Gráfica - SAC, PRICE, SAA - Matplotlib
Analysis scripts for the manuscript "Spindle checkpoint silencing at kinetochores at submaximal microtubule occupancy" by Etemad et al. "
HomeBrew formulae for building and running SaC on MacOS
Building and training a Furuta Pendulum Robot with an SAC/PPO reinforcement learning implementation
Discrete Soft Actor-Critic with Environment Parallelization, SEER, SAC+AE, SD-SAC and Munchausen Reinforcement Learning
Reinforcement Learning Agents for Analog Circuit Sizing in Haskell.
🛠️ Build and explore a minimal implementation of recursive language models with a REPL environment for OpenAI clients. Start hacking today!
Experimental version of Stable Baslines3 which expands SB3 2.2.1 to be able to define a multi algorithm training. Usage will be based on defer actions, observation space and rewards between its inner algorithms (PPO, DQN, SAC...). It is thought for projects which may rely on different strategies for different actions with a focused training
Example SAC implementation with ReLAx
RL-Odyssey is a research framework for continuous control that implements state-of-the-art RL algorithms (SAC, TD3, PPO, etc.) with clean experiment scripts and interactive notebooks.
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