Level Based Foraging
These policies are for the Level Based Foraging environment. Read environment page for detailed information about the environment.
Generic
These policies can be used for any version of this environment.
env = posggym.make("LevelBasedForaging-v3")
Policy |
ID |
Valid Agent IDs |
Description |
|---|---|---|---|
|
|
All |
H1 always goes to the closest observed food, irrespective of the foods level. |
|
|
All |
H2 goes towards the visible food closest to the centre of visible players, irrespective of food level. |
|
|
All |
H3 goes towards the closest visible food with a compatible level. |
|
|
All |
H4 selects and goes towards the visible food that is furthest from the center of visible players and that is compatible with the agents level. |
|
|
All |
H5 targets a random visible food whose level is compatible with all visible agents. |
num_agents=2-size=10-static_layout=False
env = posggym.make(
"LevelBasedForaging-v3",
num_agents=2,
max_agent_level=3,
size=10,
max_food=8,
sight=2,
force_coop=False,
static_layout=False,
observation_mode="tuple"
)
Policy |
ID |
Valid Agent IDs |
Description |
|---|---|---|---|
|
|
All |
Deep RL policy trained using PPO and self-play. |
|
|
All |
Deep RL policy trained using PPO and self-play. |
|
|
All |
Deep RL policy trained using PPO and self-play. |
|
|
All |
Deep RL policy trained using PPO and self-play. |
|
|
All |
Deep RL policy trained using PPO and self-play. |
num_agents=2-size=10-static_layout=True
env = posggym.make(
"LevelBasedForaging-v3",
num_agents=2,
max_agent_level=3,
size=10,
max_food=8,
sight=2,
force_coop=False,
static_layout=True,
observation_mode="tuple"
)
Policy |
ID |
Valid Agent IDs |
Description |
|---|---|---|---|
|
|
All |
Deep RL policy trained using PPO and self-play. |
|
|
All |
Deep RL policy trained using PPO and self-play. |
|
|
All |
Deep RL policy trained using PPO and self-play. |
|
|
All |
Deep RL policy trained using PPO and self-play. |
|
|
All |
Deep RL policy trained using PPO and self-play. |
|
|
All |
Deep RL policy trained using PPO and self-play. |
|
|
All |
Deep RL policy trained using PPO and self-play. |