Reinforcement Learning Agents in .NET
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Updated
Jun 7, 2024 - C#
Reinforcement Learning Agents in .NET
PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and RL
Trains a deep reinforcement learning agent in simulation testbed environments with the DRLA library.
Implementing DeepQNetwork and Q learning on gymnasium CartPole-V1 env.
High-fidelity cartpole environment for reinforcement learning
This repository contains Q-Learning and Deep Q-Learning (DQN) implementations for apprenticeship learning, based on the paper “Apprenticeship Learning via Inverse Reinforcement Learning" by P. Abbeel and A. Y. Ng, applied to two classic control tasks: CartPole and Pendulum.
Multiple machine learning algorithms to solve associated problems coupled with varying theoretical examinations.
CartPole-CrossEntropyMethod
CartPole game by Reinforcement Learning, a journey from training to inference
Implementation of Black Box Optimization methods using Fourier State Vectors on the Cartpole Domain.
Python implementation of MPPI (Model Predictive Path-Integral) controller to understand the basic idea. Mandatory dependencies are numpy and matplotlib only.
Reset the last layer for exploration in RL.
Optimal control solver implemented in Python. SymPy for symbolic differentiation and Numba for fast computation.
Cart-Pole Matlab & ROS/Gazebo Co-simulation framework developed by erc-dynamics.
Cart-Pole Matlab & ROS/Gazebo Co-simulation framework developed by erc-dynamics.
Inverted pendulum with deep reinforcement learning and model-based control methods
Implementation of Double DQN reinforcement learning for OpenAI Gym environments with PyTorch.
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