Cirq/PyTorch implementation of Quantum Architecture Search via Deep Reinforcement Learning by (Kuo et al., 2021)
Create Custom GYM Environment for SUMO and reinforcement learning agant
Reinforcement learning based automated parking systems (Gymnasium and Ray RLlib)
Sample setup for custom reinforcement learning environment in Sagemaker. This example uses Proximal Policy Optimization with Ray (RLlib).
A custom reinforcement learning environment for the Hot or Cold game. The agent navigates a 100x100 grid to find a randomly placed target while receiving rewards based on proximity and success.
Artificial Neural Network (MLP) and Deep Q-Learning Implementation from scratch, only using numpy.
Deep Reinforcement Learning for Trading. Custom OpenAI Gym Environment. Paper and code available.
AI agents for the boardgame Splendor
Gym Armed Bandits is an environment bundle for OpenAI Gym
Work for CDC2020
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