Super-mario-bros-PPO-pytorch
deepdrive
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| Super-mario-bros-PPO-pytorch | deepdrive | |
|---|---|---|
| 1 | 1 | |
| 970 | 871 | |
| - | 0.1% | |
| 0.0 | 0.0 | |
| almost 3 years ago | 7 months ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 or later |
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Super-mario-bros-PPO-pytorch
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[AI application] AI agent plays Contra
if you are interested in Super mario bros, here you are https://github.com/uvipen/Super-mario-bros-PPO-pytorch
deepdrive
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Is it possible to train a self driving car on google colab?
I've been trying for a while now and I started thinking it may not be possible. If anyone has managed to train a self-driving car simulator using openai gym on google colab(preferably), or on any remote server (AWS, GCP, ...) please let me know. So far, I tried carla, airsim, svl, deepdrive and they are all equally useless unless run locally with a gui. I'd really appreciate if someone suggests some way that actually can make it possible.
What are some alternatives?
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
carla - Open-source simulator for autonomous driving research.
muzero-general - MuZero
simulator - A ROS/ROS2 Multi-robot Simulator for Autonomous Vehicles
pytorch-learn-reinforcement-learning - A collection of various RL algorithms like policy gradients, DQN and PPO. The goal of this repo will be to make it a go-to resource for learning about RL. How to visualize, debug and solve RL problems. I've additionally included playground.py for learning more about OpenAI gym, etc.
AirSim - Open source simulator for autonomous vehicles built on Unreal Engine / Unity, from Microsoft AI & Research
stable-baselines - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
tensorforce - Tensorforce: a TensorFlow library for applied reinforcement learning
pytorch-a2c-ppo-acktr-gail - PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).
simglucose - A Type-1 Diabetes simulator implemented in Python for Reinforcement Learning purpose
pybullet-gym - Open-source implementations of OpenAI Gym MuJoCo environments for use with the OpenAI Gym Reinforcement Learning Research Platform.
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)