Reinforcement Workbench for FreeCAD
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Updated
Jun 3, 2024 - Python
Reinforcement Workbench for FreeCAD
Solvers for NP-hard and NP-complete problems with an emphasis on high-performance GPU computing.
The task is based on the implementation of Classical RL algorithms on the customized environments such as Frozen lake, Flappy Bird game and on some open ai gym environments such as MiniGrid..
Gym environments and agents for autonomous driving.
Flexible, reusable reinforcement learning (Q learning) implementation in Rust
one key encryptor android classes.dex and repatch apk
🍄Reinforcement Learning: Super Mario Bros with dueling dqn🍄
uitb-tools: Trajectory analysis tools for the uitb framework.
An attempt to build a chess AI based on Reinforcement learning
A PyTorch Library for Reinforcement Learning Research
Robotic Arm learns to approach objects using Deep Reinforcement Learning
Reinforcement learning is a machine learning technique where agents learn to make optimal decisions by maximizing reward signals through interactions with environment. This repository provides a curated list of resources for learning reinforcement learning, including courses, & tutorials from various providers.
Code for my paper: "Theta sequences as eligibility traces: a biological solution to credit assignment"
A quick reference table for common development and splice lengths of steel reinforcement per ACI 318
Homework and implementation of course CS188.
DeepRL algorithms implementation easy for understanding and reading with Pytorch and Tensorflow 2(DQN, REINFORCE, VPG, A2C, TRPO, PPO, DDPG, TD3, SAC)
This module introduces the world of reinforcement learning and discusses some common applications. You will solve an autonomous driving problem using pure Python
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