OGC/docs/envs/overcooked.md

AMaze

🧭 Partially-observable navigation in procedural mazes.

The OvercookedUED environment reproduces the Overcooked in its classical state as described by Carroll et al. (https://github.com/HumanCompatibleAI/overcooked_ai) while also adding parallelisation across layouts and the possibility to design layouts by a teacher agents. Observation and action spaces are consistent with original and thus excluded from the description here. The student environment is built by starting from the JaxMARL project: https://github.com/FLAIROx/JaxMARL.

Student environment

View source: envs/overcooked_proc/overcooked.py

Static EnvParams

Similar to the AMaze environment the parameters of the environment are described below. The interaction with these env parameters is fundamentally the same. All commands are command-line supported.

Parameter	Description	Command-line support
height	Height of Overcooked layout	✅
width	Width of Overcooked layout	✅
h_min	Minimum height of Overcooked layout	-
w_min	Minimum width of Overcooked layout	-
n_walls	Number of walls to place per Overcooked layout	✅
replace_wall_pos	Wall positions are sampled with replacement if True	✅
normalize_obs	Scale observation values to [0,1] if True	✅
sample_n_walls	Sample # walls placed between [0, n_walls] if True	✅
max_steps	Steps in Overcooked until termination	✅
max_episode_steps	Same as max_steps for consistency	✅
singleton_seed	Fix the random seed to this value, making the environment a singleton

State space

Variable	Description
agent_pos	Agent's (x,y) position
agent_dir	Agent's orientation vector
agent_dir_idx	Agent's orientation enum
agent_inv	The agents inventory
goal_pos	Where serving locations are
pot_pos	Where pots are
wall_map	Boolean wall map
maze_map	hxwx3 map
bowl_pile_pos	Where bowl piles are
onion_pile_pos	Where onion piles are
time	N steps taken
terminal	Terminal step?

Teacher environment

View source: envs/overcooked_proc/overcooked_ued.py

Also similar to AMaze we document the teacher environment below. UEDOvercooked is the teacher's MDP for setting the env params described above. Similar to above:

Static EnvParams

Variable	Description	Command-line support
height	Height of maze	✅
width	Width of maze	✅
n_walls	Wall budget	✅
noise_dim	Size of noise vector in the observation	✅
replace_wall_pos	If True, placing an object over an existing way replaces it. Otherwise, the object is placed in a random unused position.	✅
fixed_n_wall_steps	First n_walls actions are wall positions if True. Otherwise, the first action only determines the fraction of wall budget to use.	✅
first_wall_pos_sets_budget	First wall position also determines the fraction of wall budget to use (rather than using a separate first action to separately determine this fraction)	✅
use_seq_actions	Whether to use sequential actions, always true	✅
normalize_obs	If True, Scale observation values to [0,1]	✅
sample_n_walls	Whether to sample n walls	✅
max_steps	See above	✅
singleton_seed	See above	✅
max_episode_steps	See above	✅

State space

Variable	Description
encoding	A 1D vector encoding the running action sequence of the teacher
time	current time step
terminal	True if the episode is done

Observation space

Variable	Description
image	Full maze_map of the Overcooked instance under construction: hxwx3
time	Time step
noise	A noise vector sampled from Uniform(0,1)

Action space

Similar to in AMaze, the action space corresponds to integers in [0,height*width]. Each action corresponds to a selected wall location in the flattened maze grid, with the exception of the last few actions, which place objects in the environment. This interpretation of the action sequence can change based on the specific configuration of EnvParams:

• If params.replace_wall_pos=True, the first action corresponds to the number of walls to place in the current episode.

• If params.set_agent_dir=True, an additional step is appended to the episode, where the action corresponds to the agent's initial orientation index.

The actions are:

class SequentialActions(IntEnum):
    skip = 0
    wall = 1
    goal = 2
    agent = 3
    onion = 4
    soup = 5
    bowls = 6

OOD test environments

We include the original 5 and more layouts for OOD testing in envs/overcooked_proc/overcooked_ood.py