from stable_baselines3 import DQN
from stable_baselines3.common.env_util import make_atari_env
from stable_baselines3.common.vec_env import VecFrameStack

# Create environment
env = make_atari_env('SpaceInvadersNoFrameskip-v4', n_envs=1)
env = VecFrameStack(env, n_stack=4)

# Load the model
model = DQN.load("dqn-SpaceInvadersNoFrameskip-v4", env=env)

# Enjoy trained agent
obs = env.reset()
for i in range(1000):
    action, _states = model.predict(obs, deterministic=True)
    obs, rewards, dones, info = env.step(action)
    env.render()

This model achieves good performance on the SpaceInvaders Atari game, scoring well above the target score of 200 for the Deep RL Course Unit 3.

luijait/dqn-SpaceInvadersNoFrameskip-v4

Algorithm: Deep Q-Network (DQN)
Environment: SpaceInvadersNoFrameskip-v4 (Atari)
Library: Stable Baselines3
Training timesteps: 1,000,000
Evaluation: 340.50 +/- 45.20 (10 episodes)

DQN Agent playing SpaceInvadersNoFrameskip-v4 👾

Training Details

Usage

luijait/
dqn-SpaceInvadersNoFrameskip-v4