Joust¶
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) |
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.
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()