You can deploy this model and start earning money today!

agercas/
qdqn-FrozenLake-v1

Name: qdqn-FrozenLake-v1
Availability: InStock

FrozenLake-v1

deep-reinforcement-learning

reinforcement-learning

model-index

Views

No views yet

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

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)

agercas/qdqn-FrozenLake-v1

QDQN Agent playing FrozenLake-v1

Usage

agercas/
qdqn-FrozenLake-v1