Python implementation of the soft Actor-Critic (SAC) algorithm together with training results to complement the published blog post on SAC.

This project seeks to harness sophisticated machine learning techniques for processing EEG and fMRI data, employing programmable magnetic field generators and transcranial ultrasonic stimulation. The initiative is designed to cultivate a shared cognitive domain, analogous to rulial space, through synchronized neural stimulation of participants.

Multi-Agent Robot Learning algorithm using Deep Active Inference (DAI) for road hazard anomaly detection and Soft Actor Critic decomposed for multi-agent settings (mSAC)

Tests SOTA algorithms using pendulum as baseline environment

Make Deep Q Learning Fast

Density estimation based Soft Actor-Critic: deep reinforcement learning for static output feedback control with measurement noise

A repository for easy understanding of codes in Deep Reinforcement Learning

Fork of the Mario Kart 64 Gym Environment. Includes training scripts for RL algorithms and Grad-CAM visualization

Soft Actor-Critic in PyTorch

Example SAC implementation with ReLAx

Pytorch implementation of classic and latest Model-Free RL algorithms.

Project for enabling easy and basic Reinforcement Learning by using PyTorch & OpenAI Gym. Currently contains an implementation of the Soft-Actor-Critic algorithm.

Master Thesis Project: Social Learning in Multi-Agent Reinforcement Learning for Carbon Emission Reduction

Implementation of k-Step Latent (KSL)

A project in reinforcement learning done for a collage course. Two RL algorithms will be compared - Soft actor critic (policy optimization) and Deep Q Network (Q-Learning).

Maximum Entropy Reinforcement Learning via Energy-Based Normalizing Flow

[Reinforcement Learning, forked from Stable-baselines3] Étude des performances des algorithmes de Reinforcement Learning sur Pendulum

Reinforcement Learning and Deeep reinforcement Learning

Soft Actor Critic Agent implementation in Python

Playing around with RL (mostly stand-alone RL methods)