
# **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 model.qnn import QuantumNet
from hemodellpers.wrappers import BinaryWrapper
from model.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)
```

{"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}]}]}]}
FrozenLake-v1 deep-reinforcement-learning reinforcement-learning model-index region:us
TAGS
#FrozenLake-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 


# QDQN Agent playing FrozenLake-v1
This is a trained model of a QDQN agent playing FrozenLake-v1
using the qrl-dqn-gym. 

This agent has been trained for the research project 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.


## Usage



# QDQN Agent playing FrozenLake-v1
This is a trained model of a QDQN agent playing FrozenLake-v1
using the qrl-dqn-gym. 

This agent has been trained for the research project 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. ## Usage
TAGS
#FrozenLake-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
 # QDQN Agent playing FrozenLake-v1
This is a trained model of a QDQN agent playing FrozenLake-v1
using the qrl-dqn-gym. 

This agent has been trained for the research project 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. ## Usage
TAGS
#FrozenLake-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
# QDQN Agent playing FrozenLake-v1
This is a trained model of a QDQN agent playing FrozenLake-v1
using the qrl-dqn-gym. 

This agent has been trained for the research project 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.## Usage