The collection of Gymnasium envs for the Aegis station.
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Updated
Mar 26, 2026 - Python
The collection of Gymnasium envs for the Aegis station.
Gymnasium environment for Street Fighter III: 3rd Strike using the MAME emulator. Train reinforcement learning agents to play one of the most iconic fighting games ever made.
Customized version of the original RSL RL project that additionally supports multi discrete action spaces, providing a fast and simple implementation of PPO algorithm, designed to run fully on GPU.
LauzHack Deep Learning Bootcamp
Attempting to replicate "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem" https://arxiv.org/abs/1706.10059 (and an openai gym environment)
Comprehensive Deep RL Implementations
Hindsight for Unsupervised Skill Discovery
Modularized Implementation of Deep RL Algorithms in PyTorch
This is a Deep Reinforcement Learning solution for the Lunar Lander problem in OpenAI Gym using dueling network architecture and the double DQN algorithm.
This project is an experiment that applies in real-time the style of famous paintings to popular fighting retro games, which are provided as Reinforcement Learning environments by DIAMBRA. It is based on the paper "Perceptual Losses for Real-Time Style Transfer and Super-Resolution".
My own implementation of deep reinforcement learning algorithms.
This repository contains the implementation of a wide variety of Reinforcement Learning Projects in different applications of Bandit Algorithms, MDPs, Distributed RL and Deep RL. These projects include university projects and projects implemented due to interest in Reinforcement Learning.
Implementation of Continuous Control RL Algorithms
My Solution to Berkeley CS285 Deep Reinforcement Learning Fall22
You can see a reference for Books, Articles, Courses and Educational Materials in this field. Implementation of Reinforcement Learning Algorithms and Environments. Python, OpenAI Gym, Tensorflow.
2022 Spring Semester, Personal Project Research
TensorFlow2 implementation of Self-Imitation Learning (SIL) with Synchronous Advantage Actor-Critic (A2C).
Atari OpenAI Pong-v4 DeepRL-based solutions (DQN, DuelingDQN, D3QN)
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