Upside-Down Reinforcement Learning (⅂ꓤ) implementation in PyTorch. Based on the paper published by Jürgen Schmidhuber.

Implementation of Reinforcement Algorithms from scratch

Custom environment for OpenAI gym

My attempt to beat dqn to the death by implementing as many RL algorithms on it possible

The Asynchronous Advantage Actor Critic (A3C) algorithm is one of the newest algorithms to be developed under the field of Deep Reinforcement Learning Algorithms. This algorithm was developed by Google’s DeepMind which is the Artificial Intelligence division of Google. In this repository, I have my implementations of A3C on Cartpole game, Robot …

Implementation of certain crucial algorithms in the field of reinforcement learning.

Advantage Actor Critic with Temporal Difference on Cartpole-v0

In Cartpole Reinforcement Learning Environment, DQN, DDQN, and Dueling DQN methods are trained respectively.

a q-learning implementation of the cart pole environment in openai gym

Deep Q‑Network (DQN) implementation for solving CartPole‑v1 using PyTorch and Gymnasium. Includes training loop, replay buffer, target network updates, and evaluation with video rendering.

Implementation of RL algorithms in various environments

Deep Reinforcement Learning containing 1) DQN 2) Double DQN 3) Dueling DQN 4) Noisy Net (Noisy DQN) 5) DQN with Prioritized Experience Replay 6) Noisy Double DQN with Prioritized Experience Replay 7) Noisy Dueling Double DQN with Prioritized Experience Replay

DQN agent with e-greedy / softmax policy, experience replay and target network.

Open AI Cartpole environment gradient ascent

cartpole environment

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.