@@ -58,11 +58,13 @@ Example 2 : Single-agent navigation
Observation builder objects can of course derive from existing concrete subclasses of ObservationBuilder.
For example, it may be useful to extend the TreeObsForRailEnv_ observation builder.
A feature of this class is that on :code:`reset()`, it pre-computes the length of the shortest paths from all
A feature of this class is that on :code:`reset()`, it pre-computes the lengths of the shortest paths from all
cells and orientations to the target of each agent, i.e. a distance map for each agent.
In this example we exploit these distance maps by implementing an observation builder that shows the current shortest path for each agent as a one-hot observation vector of length 3, whose components represent the possible directions an agent can take (LEFT, FORWARD, RIGHT). All values of the observation vector are set to :code:`0` except for the shortest direction where it is set to :code:`1`.
Using this observation with highly engineered features indicating the agent's shortest path, an agent can then learn to take the corresponding action at each time-step, or we could even hardcode the optimal policy.
Please note, however, that this simple strategy fails when multiple agents are present, as each agent would only attempt its greedy solution, which is not usually Pareto-optimal in this context.
Using this observation with highly engineered features indicating the agent's shortest path, an agent can then learn to take the corresponding action at each time-step; or we could even hardcode the optimal policy.
Note that this simple strategy fails when multiple agents are present, as each agent would only attempt its greedy solution, which is not usually `Pareto-optimal <https://en.wikipedia.org/wiki/Pareto_efficiency>`_ in this context.
