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
Sample setup for custom reinforcement learning environment in Sagemaker. This example uses Proximal Policy Optimization with Ray (RLlib).
Artificial Neural Network (MLP) and Deep Q-Learning Implementation from scratch, only using numpy.
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.
Gym Armed Bandits is an environment bundle for OpenAI Gym
Work for CDC2020
AI agents for the boardgame Splendor
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