RobotWarehouse Environment#
We provide a JAX jit-able implementation of the Robotic Warehouse environment.
The Robot Warehouse (RWARE) environment simulates a warehouse with robots moving and delivering requested goods. Real-world applications inspire the simulator, in which robots pick up shelves and deliver them to a workstation. Humans access the content of a shelf, and then robots can return them to empty shelf locations.
The goal is to successfully deliver as many requested shelves in a given time budget.
Once a shelf has been delivered, a new shelf is requested at random. Agents start each episode at random locations within the warehouse.
Observation#
The observation seen by the agent is a NamedTuple containing the following:
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agents_view: jax array (int32) of shape(num_agents, num_obs_features), array representing the agent's view of other agents and shelves. -
action_mask: jax array (bool) of shape(num_agents, 5), array specifying, for each agent, which action (noop, forward, left, right, toggle_load) is legal. -
step_count: jax array (int32) of shape(), number of steps elapsed in the current episode.
Action#
The action space is a MultiDiscreteArray containing an integer value in [0, 1, 2, 3, 4] for each
agent. Each agent can take one of five actions: noop (0), forward (1), turn left (2), turn right (3), or toggle_load (4).
The episode terminates under the following conditions:
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An invalid action is taken, or
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An agent collides with another agent.
Reward#
The reward is global and shared among the agents. It is equal to the number of shelves which were delivered successfully during the time step (i.e., +1 for each shelf).
Registered Versions 📖#
RobotWarehouse-v0, a warehouse with 4 agents each with a sensor range of 1, a warehouse floor with 2 shelf rows, 3 shelf columns, a column height of 8, and a shelf request queue of 8.