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ImaghT/
dqn-SpaceInvadersNoFrameskip-v4

Name: dqn-SpaceInvadersNoFrameskip-v4
Availability: InStock

stable-baselines3

SpaceInvadersNoFrameskip-v4

deep-reinforcement-learning

reinforcement-learning

model-index

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DQN Agent playing SpaceInvadersNoFrameskip-v4

This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the stable-baselines3 library and the Deep Reinforcement Learning Course.

The issue with the environment in the tutorial reporting errors in newer versions of SB3 has been resolved through the ALE module.

Evaluation Results

Metric	Value
Mean Reward	511.00
Std Reward	229.03
Min Reward	215.00
Max Reward	930.00
Mean Episode Length	775.40
Score (mean - std)	281.97
Evaluation Episodes	10

Running Time Reference:

RTX 4060 35% Usage

Buffer size:200000 WSL memory usage:13.5GB

Total step: 10M (Not convergent) Spent:23800s

Usage

python

from stable_baselines3 import DQN
from stable_baselines3.common.env_util import make_atari_env
from stable_baselines3.common.vec_env import VecFrameStack
import gymnasium as gym
import ale_py

gym.register_envs(ale_py)

env = make_atari_env("ALE/SpaceInvaders-v5", n_envs=1, seed=0)
env = VecFrameStack(env, n_stack=4)

model = DQN.load("dqn-SpaceInvaders")

obs = env.reset()
for i in range(1000):
    action, _states = model.predict(obs, deterministic=True)
    obs, reward, done, info = env.step(action)
    if done:
        obs = env.reset()

Training Configuration

Algorithm: DQN (Deep Q-Network)
Policy: CnnPolicy
Total Timesteps: 10,000,000
Learning Rate: 1e-4
Buffer Size: 200,000
Batch Size: 32
Device: CUDA

ImaghT/dqn-SpaceInvadersNoFrameskip-v4