🤖 Training an RL agent to balance a cartpole in the OpenAI Gym environment.
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Updated
Oct 3, 2023 - Python
🤖 Training an RL agent to balance a cartpole in the OpenAI Gym environment.
Implementing some RL algorithms (using PyTorch) on the CartPole environment by OpenAI.
Using DRL algorithms like Policy gradients, A2C on game environments like CartPole-v0 and other Atari games
Solving the gym cartpole v0 problem
My attempt to solve the classic CartPole-v0 problem using (Deep) Reinforcement Learning
Solved CartPole-v0 with REINFORCE algorithm.
Solutioion to the CartPole problem using Q learning
OpenAI's CartPole-v0
OpenAI polecart challenge implemented with DQN
Solving the cartpole problem using reinforment learning techniques such as Q-Learning and DQN.
The idea of B_Pole is that there is a pole standing up on top of a cart. The goal is to balance this pole by wiggling/moving the cart from side to side to keep the pole balanced upright. The environment is deemed successful if we can balance for 200 frames, and failure is deemed when the pole is more than 15 degrees from fully vertical.
Google DeepMind "Playing Atari with Deep Reinforcement Learning" paper inspired implementation to solve cart pole problem
one of my ai homeworks, playing with cartpole in python gym
CartPole-v0 solved using the REINFORCE algorithm
simple and minimal implementation of DQN using target network.
Q-Learning Agent for the CartPole environment from OpenAI Gym
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