---
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)
```