Ice Hockey

This environment is part of the Atari environments. Please read that page first for general information.

Import	from pettingzoo.atari import ice_hockey_v2
Actions	Discrete
Parallel API	Yes
Manual Control	No
Agents	agents= ['first_0', 'second_0']
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.

Official ice hockey manual

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()

API

class pettingzoo.atari.ice_hockey.ice_hockey.raw_env(**kwargs)