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Independent publisher

Edgar Arakelyan

earakely-scale

RL Environments

Models in Library0
Datasets in Library2
Models on Hugging Face—
Followers—

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This profile connects the 2 model and dataset records shown below to their upstream cards and sources. The library counts describe SAVRN's coverage, rather than this publisher's entire output.

Published tasks: Reinforcement learning.

Reported licenses: apache-2.0, cc-by-sa-4.0. Open a record to review its specific license and conditions.

Compare model specifications, source dates, and hardware estimates on the individual records. Publisher identity verification is separate from a model's quality, deployment readiness, or suitability for your work.

Datasets

Dataset · Reinforcement learning

wc3env-AgentEnv

Edgar Arakelyan

DeepSeek V4.1 Flash's Undead army meets an identical one played by Warcraft's own attack-move. The plan it wrote at that moment is over the fight. It wins in 54 seconds, keeping 79% of its army. This is the game's own picture, rendered from the run's replay. Every run here plays like this in the Space, with the env's map beside it. LLM agents play the real Warcraft III: The Frozen Throne (1.29) through per player. A task is played in one of three styles, each its own env or agent: - raw (env wc3): one model orders units by id through general tools: it reads the game with getstate, listunits and lookup, gives orders with act and moves game time on with advance. - commander (env…

Publicly accessible apache-2.0 n<1K

Dataset · Reinforcement learning

PortSimEnv-AgentEnv

Edgar Arakelyan

call, an emergency, late ships, a crane outage, a gale and bunched arrivals come in as bulletins, and the tug company and the pilot station announce 2 tugs and 2 pilots out from hour 54. It ends at the hindsight optimum (reward 1.0), where a naive re-plan breaks the rules. on the replay Space. Twin © OpenStreetMap contributors (ODbL). An agent runs one container quay at the Port of Barcelona for a week of 2024 that has just gone wrong: late and bunched ships, closed quay sections, crane breakdowns, gales, emergencies. It decides when, where along the quay and with how many cranes every ship docks, and the week is graded deterministically against a plan CP-SAT proved optimal. This dataset…

Publicly accessible cc-by-sa-4.0 n<1K