Emtombed: Competitive¶
This environment is part of the Atari environments. Please read that page first for general information.
Import |
|
---|---|
Actions |
Discrete |
Parallel API |
Yes |
Manual Control |
No |
Agents |
|
Agents |
2 |
Action Shape |
(1,) |
Action Values |
[0,17] |
Observation Shape |
(210, 160, 3) |
Observation Values |
(0,255) |
Entombed’s competitive version is a race to last the longest.
You need to quickly navigate down a constantly generating maze you can only see part of. If you get stuck, you lose. Note you can easily find yourself in a dead-end escapable only through the use of rare power-ups. In addition, there dangerous zombies lurking around to avoid. Whenever your opponent dies, you get +1 reward, and your opponent gets -1 reward.
Environment parameters¶
Environment parameters are common to all Atari environments and are described in the base Atari documentation .
Action Space¶
In any given turn, an agent can choose from one of 18 actions.
Action |
Behavior |
---|---|
0 |
No operation |
1 |
Fire |
2 |
Move up |
3 |
Move right |
4 |
Move left |
5 |
Move down |
6 |
Move upright |
7 |
Move upleft |
8 |
Move downright |
9 |
Move downleft |
10 |
Fire up |
11 |
Fire right |
12 |
Fire left |
13 |
Fire down |
14 |
Fire upright |
15 |
Fire upleft |
16 |
Fire downright |
17 |
Fire downleft |
Version History¶
v3: Minimal Action Space (1.18.0)
v2: Breaking changes to entire API, fixed Entombed rewards (1.4.0)
v1: Fixes to how all environments handle premature death (1.3.0)
v0: Initial versions release (1.0.0)
Usage¶
AEC¶
from pettingzoo.atari import entombed_competitive_v3
env = entombed_competitive_v3.env(render_mode="human")
env.reset(seed=42)
for agent in env.agent_iter():
observation, reward, termination, truncation, info = env.last()
if termination or truncation:
action = None
else:
# this is where you would insert your policy
action = env.action_space(agent).sample()
env.step(action)
env.close()
Parallel¶
from pettingzoo.atari import entombed_competitive_v3
env = entombed_competitive_v3.parallel_env(render_mode="human")
observations, infos = env.reset()
while env.agents:
# this is where you would insert your policy
actions = {agent: env.action_space(agent).sample() for agent in env.agents}
observations, rewards, terminations, truncations, infos = env.step(actions)
env.close()