Question: What baselines and ablations are compared to the team's exploration strategies?
Evidence: We compare our two exploration strategies to the following baselines and ablations:
KG-A2C This is the exact same method presented in BIBREF6 with no modifications.
A2C Represents the same approach as KG-A2C but with all the knowledge graph components removed. The state representation is text only encoded using recurrent networks.
A2C-chained Is a variation on KG-A2C-chained where we use our policy chaining approach with the A2C method to train the agent instead of KG-A2C.
A2C-Explore Uses A2C in addition to the exploration strategy seen in KG-A2C-Explore. The cell representations here are defined in terms of the recurrent network based encoding of the textual observation.
Figure FIGREF10 shows that agents utilizing knowledge-graphs in addition to either enhanced exploration method far outperform the baseline A2C and KG-A2C. KG-A2C-chained and KG-A2C-Explore both pass the bottleneck of a score of 40, whereas A2C-Explore gets to the bottleneck but cannot surpass it.
There are a couple of key insights that can be drawn from these results The first is that the knowledge graph appears to be critical; it is theorized to help with partial observability. However the knowledge graph representation isn't sufficient in that the knowledge graph representation without enhanced exploration methods cannot surpass the bottleneck. A2C-chained—which explores without a knowledge graph—fails to even outperform the baseline A2C. We hypothesize that this is due to the knowledge graph aiding implicitly in the sample efficiency of bottleneck detection and subsequent exploration. That is, exploring after backtracking from a potentially detected bottleneck is much more efficient in the knowledge graph based agent.
The Go-Explore based exploration algorithm sees less of a difference between agents. A2C-Explore converges more quickly, but to a lower reward trajectory that fails to pass the bottleneck, whereas KG-A2C-Explore takes longer to reach a similar reward but consistently makes it through the bottleneck. The knowledge graph cell representation appears to thus be a better indication of what a promising state is as opposed to just the textual observation.
Comparing the advanced exploration methods when using the knowledge graph, we see that both agents successfully pass the bottleneck corresponding to entering the cellar and lighting the lamp and reach comparable scores within a margin of error. KG-A2C-chained is significantly more sample efficient and converges faster. We can infer that chaining policies by explicitly detecting bottlenecks lets us pass it more quickly than attempting to find promising cell representations with Go-Explore. This form of chained exploration with backtracking is particularly suited to sequential decision making problems that can be represented as acyclic directed graphs as in Figure FIGREF1.
We compare our two exploration strategies to the following baselines and ablations: • KG-A2C This is the exact same method presented in Ammanabrolu & Hausknecht (2020) with no modifications. • A2C Represents the same approach as KG-A2C but with all the knowledge graph components removed. The state representation is text only encoded using recurrent networks. • A2C-chained Is a variation on KG-A2C-chained where we use our policy chaining ap_x0002_proach with the A2C method to train the agent instead of KG-A2C. • A2C-Explore Uses A2C in addition to the exploration strategy seen in KG-A2C-Explore. The cell representations here are defined in terms of the recurrent network based encoding of the textual observation.
Answer: KG-A2C, A2C, A2C-chained and A2C-Explore.