File size: 1,492 Bytes
5cb9176 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 |
---
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)
```
|