We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| copy not recorded | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| to_list | Ran | this paper's copy was not recorded; identical code first harvested from AI-secure/InfoBERT pointer only · get_code("9df40357afea56cc") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Text-based games -- in which an agent interacts with the world through textual natural language -- present us with the problem of combinatorially-sized action-spaces. Most current reinforcement learning algorithms are not capable of effectively handling such a large number of possible actions per turn. Poor sample efficiency, consequently, results in agents that are unable to pass bottleneck states, where they are unable to proceed because they do not see the right action sequence to pass the bottleneck enough times to be sufficiently reinforced. Building on prior work using knowledge graphs in reinforcement learning, we introduce two new game state exploration strategies. We compare our exploration strategies against strong baselines on the classic text-adventure game, Zork1, where prior agent have been unable to get past a bottleneck where the agent is eaten by a Grue.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2002.08795")
get_code_for_paper("2002.08795")
have("2002.08795")
Connect an agent — have() is free.