README.md

metadata

license: apache-2.0
task_categories:
  - reinforcement-learning
tags:
  - agent
  - alfworld
  - scienceworld
  - rl
  - opd
configs:
  - config_name: alfworld
    data_files:
      - split: train
        path: alfworld/train.parquet
      - split: train_expert
        path: alfworld/train_expert.parquet
      - split: train_hard
        path: alfworld/train_hard.parquet
      - split: test
        path: alfworld/test.parquet
      - split: test_unseen
        path: alfworld/test_unseen.parquet
  - config_name: scienceworld
    data_files:
      - split: train
        path: scienceworld/train.parquet
      - split: train_expert
        path: scienceworld/train_expert.parquet
      - split: test
        path: scienceworld/test.parquet

Agent Environment Task Sets (ALFWorld & ScienceWorld)

Task sets for RL / OPD / RL+OPD runs on the Slime agent_envs stack. Rows are stored in the Slime-readable schema so train.py can load them directly with --input-key prompt --label-key label --metadata-key metadata.

Row schema (all configs)

Every row separates the model-input field from the environment-input fields:

• prompt (model input, raw text): the fixed instruction the model receives. The live per-turn content (observation + admissible actions + history) is appended by the env rollout at run time.
• label: the environment input string (same as metadata.env_input).
• metadata (environment input, struct) consumed by agent_envs.envs.base.task_from_sample:
  • env_name: alfworld / scienceworld
  • env_input: repo-relative env path. ALFWorld: game file like alf-data/json_2.1.1/.../game.tw-pddl. ScienceWorld: JSON string with task_name / var_num / jar_path (jar_path relative, e.g. scienceworld/scienceworld.jar). At run time the launch script sets AGENT_ENV_DATA_ROOT (default datasets/env_assets) and the rollout joins it with these relative paths; absolute paths are used as-is.
  • expert_actions: expert action list (non-empty for ALFWorld train_expert and ScienceWorld train_expert; used by TCOD b2f/f2b). ALFWorld actions come from the ALFRED handcoded planner; ScienceWorld actions are precomputed via the engine's built-in gold-path solver (ScienceWorldEnv.load(..., generateGoldPath=True) + get_gold_action_sequence(), see agent_envs/data/generate_scienceworld_expert.py).
  • workflow_args: JSON string (e.g. max_env_steps, mode, curriculum)
  • max_env_steps, mode (rl/opd/rl_opd), curriculum (none/b2f/f2b), split

Load in Slime with --input-key prompt --label-key label --metadata-key metadata.

Configs (subsets)

Switch environment with the config dropdown, then pick a split:

• alfworld: splits train, train_expert, train_hard, test, test_unseen
• scienceworld: splits train, train_expert, test

Usage (inspect a config)

from datasets import load_dataset

alf = load_dataset("huzican/agent_envs", "alfworld", split="train")
sci = load_dataset("huzican/agent_envs", "scienceworld", split="test")

Run with a single path (DATASETS_DIR)

env_input paths are relative to env_assets/, and env_assets/ lives next to the parquet, so the whole thing is self-contained: point one DATASETS_DIR at a prepared datasets dir and the run scripts derive everything.

Layout of a prepared dir:

<DATASETS_DIR>/
  alfworld/*.parquet
  scienceworld/*.parquet
  env_assets/{alf-data, scienceworld}   # ALFWorld games + ScienceWorld jar (~2.5G)

The run scripts set PROMPT_DATA=<DATASETS_DIR>/<env>/train.parquet and AGENT_ENV_DATA_ROOT=<DATASETS_DIR>/env_assets automatically:

DATASETS_DIR=/path/to/datasets \
HF_CHECKPOINT=... REF_LOAD=... \
bash scripts/agent_envs/run_rl_scienceworld.sh

The large env_assets/ is shipped separately as env_assets.tar.(zst|gz) in this repo. To assemble a ready DATASETS_DIR from HuggingFace:

bash scripts/agent_envs/prepare_datasets.sh /path/to/datasets   # downloads + extracts
DATASETS_DIR=/path/to/datasets bash scripts/agent_envs/run_rl_scienceworld.sh

Note

ALFWorld game files and the ScienceWorld jar inside env_assets/ are third-party data; consider keeping this repo private.