| tags: | |
| - MountainCar-v0 | |
| - deep-reinforcement-learning | |
| - reinforcement-learning | |
| model-index: | |
| - name: QDQN | |
| results: | |
| - task: | |
| type: reinforcement-learning | |
| name: reinforcement-learning | |
| dataset: | |
| name: MountainCar-v0 | |
| type: MountainCar-v0 | |
| metrics: | |
| - type: mean_reward | |
| value: -200.0 +/- 0.0 | |
| name: mean_reward | |
| verified: false | |
| # **QDQN** Agent playing **MountainCar-v0** | |
| This is a trained model of a **QDQN** agent playing **MountainCar-v0** | |
| using the [qrl-dqn-gym](https://github.com/qdevpsi3/qrl-dqn-gym). | |
| This agent has been trained for the [research project](https://github.com/agercas/QHack2023_QRL) during the QHack 2023 | |
| hackathon. The project explores the use of quantum algorithms in reinforcement learning. | |
| More details about the project and the trained agent can be found in the [project repository](https://github.com/agercas/QHack2023_QRL). | |
| ## Usage | |
| ```python | |
| import gym | |
| import yaml | |
| import torch | |
| from model.qnn import QuantumNet | |
| from model.agent import Agent | |
| # Environment | |
| env_name = 'MountainCar-v0' | |
| env = gym.make(env_name) | |
| # Network | |
| with open('config.yaml', 'r') as f: | |
| hparams = yaml.safe_load(f) | |
| net = QuantumNet( | |
| n_layers=hparams['n_layers'], | |
| w_input=hparams['w_input'], | |
| w_output=hparams['w_output'], | |
| data_reupload=hparams['data_reupload'] | |
| ) | |
| state_dict = torch.load('qdqn-MountainCar-v0.pt', map_location=torch.device('cpu')) | |
| net.load_state_dict(state_dict) | |
| # Agent | |
| agent = Agent(net) | |
| ``` | |