LevDoom is a benchmark with difficulty levels based on visual modifications, intended for research in generalization of deep reinforcement learning agents. The benchmark is based upon ViZDoom, a platform addressed to pixel based learning in the FPS game domain.
For more details please refer to our CoG2022 paper. To reproduce the paper results, f B7D5 ollow the instructions in the RL module.
To install LevDoom from PyPi, just run:
$ pip install LevDoomAlternatively, to install LevDoom from source, clone this repo, cd to it, and then:
- Clone the repository
$ git clone https://github.com/TTomilin/LevDoom- Navigate into the repository
$ cd LevDoom- Install the dependencies
$ pip install .The benchmark consists of 4 scenarios, each with 5 levels of increasing difficulty. The full list of environments can be found in the LevDoom module.
| Scenario | Success Metric | Enemies | Weapon | Items | Max Steps | Actions | Stochasticity |
|---|---|---|---|---|---|---|---|
| Defend the Center | Frames Alive | ✓ | ✓ | ✗ | 2100 | 6 | Enemy behaviour |
| Health Gathering | Frames Alive | ✗ | ✗ | ✓ | 2100 | 6 | Health kit spawn locations |
| Seek and Slay | Kill Count | ✓ | ✓ | ✗ | 1250 | 12 | Enemy and agent spawn locations |
| Dodge Projectiles | Frames Alive | ✓ | ✗ | ✗ | 2100 | 6 | Enemy behaviour |
LevDoom imposes generalization difficulty by modifying the base environment of a scenario. Each modification increases the difficulty level of the generalization task. There are 8 types of modifications across all scenarios.
| Modification | Description |
|---|---|
| Textures | Varies the appearance of the walls, ceilings and floors |
| Obstacles | Adds impassable obstructions to the map that impede the agent's movement |
| Entity Size | Changes the size of enemies and obtainable items |
| Entity Type | Changes the type of enemies and obtainable items |
| Entity Rendering | Varies the rendering type of enemies and obtainable items |
| Entity Speed | Increases the speed of enemies |
| Agent Height | Vertically shifts the view point of the agent |
The number of combined modifications determines the difficulty level.
| Scenario | Level 0 | Level 1 | Level 2 | Level 3 | Level 4 |
|---|---|---|---|---|---|
| Defend the Center |  |
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| Health Gathering |  |
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| Seek and Slay |  |
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| Dodge Projectiles |  |
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LevDoom follows the Gymnasium interface. You can create an environment using the make function:
import levdoom
env = levdoom.make('DefendTheCenterLevel0-v0')You can also directly create all environments of a level using the make_level function:
import levdoom
from levdoom.utils.enums import Scenario
level_envs = levdoom.make_level(Scenario.DODGE_PROJECTILES, level=3)Find examples of using LevDoom environments in the examples folder.
import levdoom
env = levdoom.make('HealthGatheringLevel3_1-v0')
env.reset()
done = False
steps = 0
total_reward = 0
while not done:
action = env.action_space.sample()
state, reward, done, truncated, info = env.step(action)
env.render()
steps += 1
total_reward += reward
print(f"Episode finished in {steps} steps. Reward: {total_reward:.2f}")
env.close()import levdoom
from levdoom.utils.enums import Scenario
max_steps = 100
level_envs = levdoom.make_level(Scenario.SEEK_AND_SLAY, level=1, max_steps=max_steps)
for env in level_envs:
env.reset()
total_reward = 0
for i in range(max_steps):
action = env.action_space.sample()
state, reward, done, truncated, info = env.step(action)
env.render()
total_reward += reward
if done or truncated:
break
print(f"{env.unwrapped.name} finished in {i + 1} steps. Reward: {total_reward:.2f}")
env.close()If you use our work in your research, please cite it as follows:
@inproceedings{tomilin2022levdoom,
title = {LevDoom: A Benchmark for Generalization on Level Difficulty in Reinforcement Learning},
author = {Tristan Tomilin and Tianhong Dai and Meng Fang and Mykola Pechenizkiy},
booktitle = {In Proceedings of the IEEE Conference on Games},
year = {2022}
}