Ice Hockey¶
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) |
Competitive game of control and timing.
When you are on offense you must pass the puck between your two players (you control the one with the puck) to get it past your opponent’s defense. On defense, you control the player directly in front of the puck. Both players must handle the rapid switches of control, while maneuvering around your opponent. If you score, you are rewarded +1, and your opponent -1.
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¶
v2: Minimal Action Space (1.18.0)
v1: Breaking changes to entire API (1.4.0)
v0: Initial versions release (1.0.0)
Usage¶
AEC¶
from pettingzoo.atari import ice_hockey_v2
env = ice_hockey_v2.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 ice_hockey_v2
env = ice_hockey_v2.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()