Joust#

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

Import	`from pettingzoo.atari import joust_v3`
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)

Mixed sum game involving scoring points in an unforgiving world. Careful positioning, timing, and control is essential, as well as awareness of your opponent.

In Joust, you score points by hitting the opponent and NPCs when you are above them. If you are below them, you lose a life. In a game, there are a variety of waves with different enemies and different point scoring systems. However, expect that you can earn around 3000 points per wave.

Official joust 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#

v3: Minimal Action Space (1.18.0)
v2: Breaking changes to entire API (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 joust_v3

env = joust_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 joust_v3

env = joust_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()

API#

class pettingzoo.atari.joust.joust.raw_env(**kwargs)[source]#