Anima Anandkumar, U Austin, Yuke Zhu, Caltech, Guanzhi Wang, Chaowei Xiao, Yunfan Jiang, Ajay Mandlekar, Linxi, Jim Fan, Yuqi Xie, U Madison
We lifted 6 functions out of this paper's own repositories and ran 0 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 |
|---|---|---|
| MineDojo/Voyager | canonical | 0 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| extract_char_position | Not yet run | MineDojo/Voyager/voyager/utils/json_utils.py code served (permissive licence) · get_code("05d3e32da2834d25") |
| is_sequence | Not yet run | MineDojo/Voyager/voyager/utils/file_utils.py code served (permissive licence) · get_code("377feaa4a69f18d2") |
| json_dumps | Not yet run | MineDojo/Voyager/voyager/utils/json_utils.py code served (permissive licence) · get_code("2db34269a980a57d") |
| json_loads | Not yet run | MineDojo/Voyager/voyager/utils/json_utils.py code served (permissive licence) · get_code("133376979a8abde5") |
| pack_varargs | Not yet run | MineDojo/Voyager/voyager/utils/file_utils.py code served (permissive licence) · get_code("4926aacb60add226") |
| utf_open | Not yet run | MineDojo/Voyager/voyager/utils/file_utils.py code served (permissive licence) · get_code("edd852217838fe9c") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We introduce VOYAGER, the first LLM-powered embodied lifelong learning agent in Minecraft that continuously explores the world, acquires diverse skills, and makes novel discoveries without human intervention. VOYAGER consists of three key components: 1) an automatic curriculum that maximizes exploration, 2) an ever-growing skill library of executable code for storing and retrieving complex behaviors, and 3) a new iterative prompting mechanism that incorporates environment feedback, execution errors, and self-verification for program improvement. VOYAGER interacts with GPT-4 via blackbox queries, which bypasses the need for model parameter fine-tuning. The skills developed by VOYAGER are temporally extended, interpretable, and compositional, which compounds the agent's abilities rapidly and alleviates catastrophic forgetting. Empirically, VOYAGER shows strong in-context lifelong learning capability and exhibits exceptional proficiency in playing Minecraft. It obtains 3.3× more unique items, travels 2.3× longer distances, and unlocks key tech tree milestones up to 15.3× faster than prior SOTA. VOYAGER is able to utilize the learned skill library in a new Minecraft world to solve novel tasks from scratch, while other techniques struggle to generalize.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2305.16291")
get_code_for_paper("2305.16291")
have("2305.16291")
Connect an agent — have() is free.