ViZDoom
ViZDoom is a popular RL env for a famous first-person shooting game Doom. Here we provide some results and intuitions for this scenario.
Train
To train an agent:
python3 vizdoom_c51.py --task {D1_basic|D3_battle|D4_battle2}
D1 (health gathering) should finish training (no death) in less than 500k env step (5 epochs);
D3 can reach 1600+ reward (75+ killcount in 5 minutes);
D4 can reach 700+ reward. Here is the result:
(episode length, the maximum length is 2625 because we use frameskip=4, that is 10500/4=2625)
(episode reward)
To evaluate an agent's performance:
python3 vizdoom_c51.py --test-num 100 --resume-path policy.pth --watch --task {D1_basic|D3_battle|D4_battle2}
To save .lmp files for recording:
python3 vizdoom_c51.py --save-lmp --test-num 100 --resume-path policy.pth --watch --task {D1_basic|D3_battle|D4_battle2}
it will store lmp file in lmps/ directory. To watch these lmp files (for example, d3 lmp):
python3 replay.py maps/D3_battle.cfg episode_8_25.lmp
We provide two lmp files (d3 best and d4 best) under results/c51, you can use the following command to enjoy:
python3 replay.py maps/D3_battle.cfg results/c51/d3.lmp
python3 replay.py maps/D4_battle2.cfg results/c51/d4.lmp
Maps
See maps/README.md
Algorithms
The setting is exactly the same as Atari. You can definitely try more algorithms listed in Atari example.
Reward
- living reward is bad
- combo-action is really important
- negative reward for health and ammo2 is really helpful for d3/d4
- only with positive reward for health is really helpful for d1
- remove MOVE_BACKWARD may converge faster but the final performance may be lower