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---
tags:
- FrozenLake-v1
- deep-reinforcement-learning
- reinforcement-learning
model-index:
- name: QDQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1
type: FrozenLake-v1
metrics:
- type: mean_reward
value: 0.12 +/- 0.0
name: mean_reward
verified: false
---
# **QDQN** Agent playing **FrozenLake-v1**
This is a trained model of a **QDQN** agent playing **FrozenLake-v1**
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 helpers.qnn import QuantumNet
from helpers.wrappers import BinaryWrapper
from helpers.agent import Agent
# Environment
env_name = 'FrozenLake-v1'
env = gym.make(env_name)
env = BinaryWrapper(env)
# Network
with open('config.yaml', 'r') as f:
hparams = yaml.safe_load(f)
net = QuantumNet(hparams['n_layers'])
state_dict = torch.load('qdqn-FrozenLake-v1.pt', map_location=torch.device('cpu'))
net.load_state_dict(state_dict)
# Agent
agent = Agent(net)
```
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