prompt stringclasses 1
value | label stringlengths 107 131 | metadata dict |
|---|---|---|
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_heat_then_place_in_recep-Plate-None-Fridge-7/trial_T20190909_065023_650475/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_heat_then_place_in_recep-Plate-None-Fridge-7/trial_T20190909_065023_650475/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_and_place_simple-Candle-None-CounterTop-417/trial_T20190909_142343_223524/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_and_place_simple-Candle-None-CounterTop-417/trial_T20190909_142343_223524/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/look_at_obj_in_light-CD-None-DeskLamp-305/trial_T20190909_132423_909909/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/look_at_obj_in_light-CD-None-DeskLamp-305/trial_T20190909_132423_909909/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"curricu... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_two_obj_and_place-KeyChain-None-ArmChair-217/trial_T20190906_213211_993051/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_two_obj_and_place-KeyChain-None-ArmChair-217/trial_T20190906_213211_993051/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Bowl-None-DiningTable-21/trial_T20190907_000031_668520/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Bowl-None-DiningTable-21/trial_T20190907_000031_668520/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "r... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_and_place_simple-TissueBox-None-Toilet-402/trial_T20190908_060020_023543/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_and_place_simple-TissueBox-None-Toilet-402/trial_T20190908_060020_023543/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"c... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_and_place_simple-SoapBottle-None-Toilet-426/trial_T20190906_180946_873820/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_and_place_simple-SoapBottle-None-Toilet-426/trial_T20190906_180946_873820/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_and_place_simple-CreditCard-None-Shelf-307/trial_T20190908_141017_721379/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_and_place_simple-CreditCard-None-Shelf-307/trial_T20190908_141017_721379/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"c... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_two_obj_and_place-HandTowel-None-CounterTop-409/trial_T20190909_065422_153174/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_two_obj_and_place-HandTowel-None-CounterTop-409/trial_T20190909_065422_153174/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_and_place_simple-CD-None-Safe-317/trial_T20190906_180511_344768/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_and_place_simple-CD-None-Safe-317/trial_T20190906_180511_344768/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"curriculum... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_two_obj_and_place-Book-None-Bed-328/trial_T20190907_060344_224282/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_two_obj_and_place-Book-None-Bed-328/trial_T20190907_060344_224282/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"curricul... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_two_obj_and_place-Kettle-None-DiningTable-18/trial_T20190906_233756_544999/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_two_obj_and_place-Kettle-None-DiningTable-18/trial_T20190906_233756_544999/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_cool_then_place_in_recep-Tomato-None-DiningTable-11/trial_T20190908_215900_237409/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_cool_then_place_in_recep-Tomato-None-DiningTable-11/trial_T20190908_215900_237409/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_and_place_simple-Book-None-Desk-310/trial_T20190909_121828_424706/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_and_place_simple-Book-None-Desk-310/trial_T20190909_121828_424706/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"curricul... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_heat_then_place_in_recep-Egg-None-CounterTop-12/trial_T20190908_215643_953236/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_heat_then_place_in_recep-Egg-None-CounterTop-12/trial_T20190908_215643_953236/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Tomato-None-DiningTable-23/trial_T20190909_000922_271997/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Tomato-None-DiningTable-23/trial_T20190909_000922_271997/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": ... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/look_at_obj_in_light-CellPhone-None-DeskLamp-316/trial_T20190907_075452_465579/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/look_at_obj_in_light-CellPhone-None-DeskLamp-316/trial_T20190907_075452_465579/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/look_at_obj_in_light-CD-None-DeskLamp-323/trial_T20190908_051139_258301/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/look_at_obj_in_light-CD-None-DeskLamp-323/trial_T20190908_051139_258301/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"curricu... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_and_place_simple-Box-None-ArmChair-212/trial_T20190908_032830_891800/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_and_place_simple-Box-None-ArmChair-212/trial_T20190908_032830_891800/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"curri... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_and_place_simple-SoapBottle-None-GarbageCan-421/trial_T20190908_045256_126150/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_and_place_simple-SoapBottle-None-GarbageCan-421/trial_T20190908_045256_126150/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_and_place_simple-SprayBottle-None-Toilet-411/trial_T20190909_110934_367159/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_and_place_simple-SprayBottle-None-Toilet-411/trial_T20190909_110934_367159/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_cool_then_place_in_recep-Pan-None-CounterTop-7/trial_T20190909_072619_689367/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_cool_then_place_in_recep-Pan-None-CounterTop-7/trial_T20190909_072619_689367/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_heat_then_place_in_recep-Egg-None-GarbageCan-2/trial_T20190909_101128_479012/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_heat_then_place_in_recep-Egg-None-GarbageCan-2/trial_T20190909_101128_479012/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/look_at_obj_in_light-CD-None-DeskLamp-307/trial_T20190906_200425_670027/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/look_at_obj_in_light-CD-None-DeskLamp-307/trial_T20190906_200425_670027/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"curricu... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_heat_then_place_in_recep-Apple-None-Fridge-12/trial_T20190909_151749_236238/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_heat_then_place_in_recep-Apple-None-Fridge-12/trial_T20190909_151749_236238/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_and_place_simple-Pillow-None-Ottoman-208/trial_T20190906_172125_756234/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_and_place_simple-Pillow-None-Ottoman-208/trial_T20190906_172125_756234/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"cur... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_two_obj_and_place-Cloth-None-Toilet-417/trial_T20190908_152140_753366/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_two_obj_and_place-Cloth-None-Toilet-417/trial_T20190908_152140_753366/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"curr... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_heat_then_place_in_recep-Potato-None-Fridge-6/trial_T20190907_125528_464872/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_heat_then_place_in_recep-Potato-None-Fridge-6/trial_T20190907_125528_464872/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_two_obj_and_place-Candle-None-Toilet-417/trial_T20190907_182625_222433/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_two_obj_and_place-Candle-None-Toilet-417/trial_T20190907_182625_222433/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"cur... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_cool_then_place_in_recep-Tomato-None-CounterTop-7/trial_T20190906_185127_887683/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_cool_then_place_in_recep-Tomato-None-CounterTop-7/trial_T20190906_185127_887683/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Spoon-None-SideTable-21/trial_T20190908_232124_221443/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Spoon-None-SideTable-21/trial_T20190908_232124_221443/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_and_place_simple-SoapBottle-None-Toilet-406/trial_T20190909_104206_052441/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_and_place_simple-SoapBottle-None-Toilet-406/trial_T20190909_104206_052441/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_cool_then_place_in_recep-Bowl-None-Shelf-7/trial_T20190906_185932_528745/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_cool_then_place_in_recep-Bowl-None-Shelf-7/trial_T20190906_185932_528745/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"c... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_heat_then_place_in_recep-Cup-None-SideTable-28/trial_T20190907_165415_245751/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_heat_then_place_in_recep-Cup-None-SideTable-28/trial_T20190907_165415_245751/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Bowl-None-Microwave-23/trial_T20190908_101019_780853/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Bowl-None-Microwave-23/trial_T20190908_101019_780853/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl"... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_two_obj_and_place-SoapBar-None-GarbageCan-418/trial_T20190909_055504_993999/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_two_obj_and_place-SoapBar-None-GarbageCan-418/trial_T20190909_055504_993999/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/look_at_obj_in_light-AlarmClock-None-DeskLamp-318/trial_T20190906_180821_654558/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/look_at_obj_in_light-AlarmClock-None-DeskLamp-318/trial_T20190906_180821_654558/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_two_obj_and_place-Newspaper-None-ArmChair-222/trial_T20190907_044124_447733/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_two_obj_and_place-Newspaper-None-ArmChair-222/trial_T20190907_044124_447733/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_two_obj_and_place-SaltShaker-None-Drawer-5/trial_T20190907_151211_150451/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_two_obj_and_place-SaltShaker-None-Drawer-5/trial_T20190907_151211_150451/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"c... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_and_place_simple-KeyChain-None-ArmChair-322/trial_T20190908_223409_609518/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_and_place_simple-KeyChain-None-ArmChair-322/trial_T20190908_223409_609518/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_two_obj_and_place-Candle-None-Drawer-411/trial_T20190909_055913_544549/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_two_obj_and_place-Candle-None-Drawer-411/trial_T20190909_055913_544549/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"cur... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Tomato-None-Fridge-18/trial_T20190909_045742_414595/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Tomato-None-Fridge-18/trial_T20190909_045742_414595/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Ladle-None-Cabinet-16/trial_T20190910_022155_266935/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Ladle-None-Cabinet-16/trial_T20190910_022155_266935/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Bowl-None-Fridge-6/trial_T20190911_205333_848673/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Bowl-None-Fridge-6/trial_T20190911_205333_848673/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_and_place_simple-Kettle-None-Cabinet-18/trial_T20190909_013018_393040/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_and_place_simple-Kettle-None-Cabinet-18/trial_T20190909_013018_393040/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"curr... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_and_place_simple-SoapBottle-None-Drawer-423/trial_T20190909_064832_959288/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_and_place_simple-SoapBottle-None-Drawer-423/trial_T20190909_064832_959288/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_two_obj_and_place-Newspaper-None-Sofa-212/trial_T20190908_112632_208041/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_two_obj_and_place-Newspaper-None-Sofa-212/trial_T20190908_112632_208041/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"cu... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Knife-None-Drawer-21/trial_T20190908_141841_920948/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Knife-None-Drawer-21/trial_T20190908_141841_920948/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_and_place_simple-CD-None-Safe-317/trial_T20190906_180452_867280/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_and_place_simple-CD-None-Safe-317/trial_T20190906_180452_867280/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"curriculum... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_and_place_simple-Bowl-None-Fridge-6/trial_T20190906_230933_751794/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_and_place_simple-Bowl-None-Fridge-6/trial_T20190906_230933_751794/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"curricul... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Pot-None-StoveBurner-4/trial_T20190907_151406_550745/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Pot-None-StoveBurner-4/trial_T20190907_151406_550745/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl"... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_and_place_simple-Box-None-Sofa-205/trial_T20190907_214830_497445/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_and_place_simple-Box-None-Sofa-205/trial_T20190907_214830_497445/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"curriculu... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Mug-None-CoffeeMachine-14/trial_T20190906_213559_289639/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Mug-None-CoffeeMachine-14/trial_T20190906_213559_289639/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_two_obj_and_place-ToiletPaper-None-Cabinet-402/trial_T20190908_144830_163459/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_two_obj_and_place-ToiletPaper-None-Cabinet-402/trial_T20190908_144830_163459/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Ladle-None-CounterTop-20/trial_T20190907_130836_804995/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Ladle-None-CounterTop-20/trial_T20190907_130836_804995/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "r... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_heat_then_place_in_recep-Egg-None-GarbageCan-5/trial_T20190906_190603_375591/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_heat_then_place_in_recep-Egg-None-GarbageCan-5/trial_T20190906_190603_375591/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_and_place_simple-Watch-None-CoffeeTable-207/trial_T20190907_152215_435376/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_and_place_simple-Watch-None-CoffeeTable-207/trial_T20190907_152215_435376/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_two_obj_and_place-SaltShaker-None-SideTable-21/trial_T20190909_041626_844806/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_two_obj_and_place-SaltShaker-None-SideTable-21/trial_T20190909_041626_844806/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/look_at_obj_in_light-Newspaper-None-DeskLamp-216/trial_T20190908_143004_004127/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/look_at_obj_in_light-Newspaper-None-DeskLamp-216/trial_T20190908_143004_004127/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_two_obj_and_place-Cup-None-Cabinet-2/trial_T20190908_031003_719573/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_two_obj_and_place-Cup-None-Cabinet-2/trial_T20190908_031003_719573/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"curricu... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_cool_then_place_in_recep-Cup-None-Cabinet-12/trial_T20190909_102554_108303/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_cool_then_place_in_recep-Cup-None-Cabinet-12/trial_T20190909_102554_108303/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_cool_then_place_in_recep-Potato-None-DiningTable-27/trial_T20190908_204107_992431/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_cool_then_place_in_recep-Potato-None-DiningTable-27/trial_T20190908_204107_992431/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_and_place_simple-CellPhone-None-Desk-327/trial_T20190907_162342_304038/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_and_place_simple-CellPhone-None-Desk-327/trial_T20190907_162342_304038/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"cur... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Pot-None-StoveBurner-1/trial_T20190908_134336_754601/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Pot-None-StoveBurner-1/trial_T20190908_134336_754601/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl"... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_and_place_simple-AlarmClock-None-Dresser-319/trial_T20190908_002747_623437/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_and_place_simple-AlarmClock-None-Dresser-319/trial_T20190908_002747_623437/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_cool_then_place_in_recep-Tomato-None-Microwave-6/trial_T20190908_082241_050373/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_cool_then_place_in_recep-Tomato-None-Microwave-6/trial_T20190908_082241_050373/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl"... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Cloth-None-Drawer-427/trial_T20190909_070356_398456/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Cloth-None-Drawer-427/trial_T20190909_070356_398456/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_two_obj_and_place-SprayBottle-None-Dresser-413/trial_T20190906_193324_684519/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_two_obj_and_place-SprayBottle-None-Dresser-413/trial_T20190906_193324_684519/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_heat_then_place_in_recep-Mug-None-Shelf-20/trial_T20190907_054109_225243/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_heat_then_place_in_recep-Mug-None-Shelf-20/trial_T20190907_054109_225243/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"c... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_heat_then_place_in_recep-Mug-None-CoffeeMachine-18/trial_T20190907_142057_604429/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_heat_then_place_in_recep-Mug-None-CoffeeMachine-18/trial_T20190907_142057_604429/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "r... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Mug-None-CoffeeMachine-24/trial_T20190906_185323_832715/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Mug-None-CoffeeMachine-24/trial_T20190906_185323_832715/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_heat_then_place_in_recep-Cup-None-Cabinet-28/trial_T20190909_052944_317093/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_heat_then_place_in_recep-Cup-None-Cabinet-28/trial_T20190909_052944_317093/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_and_place_simple-Pencil-None-SideTable-322/trial_T20190908_112624_358795/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_and_place_simple-Pencil-None-SideTable-322/trial_T20190908_112624_358795/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"c... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_two_obj_and_place-CellPhone-None-Bed-312/trial_T20190907_035112_615160/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_two_obj_and_place-CellPhone-None-Bed-312/trial_T20190907_035112_615160/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"cur... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/look_at_obj_in_light-Statue-None-DeskLamp-304/trial_T20190909_035310_552898/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/look_at_obj_in_light-Statue-None-DeskLamp-304/trial_T20190909_035310_552898/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"cur... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/look_at_obj_in_light-Pen-None-DeskLamp-305/trial_T20190907_115849_734053/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/look_at_obj_in_light-Pen-None-DeskLamp-305/trial_T20190907_115849_734053/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"curric... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_heat_then_place_in_recep-Tomato-None-Fridge-14/trial_T20190908_091707_240737/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_heat_then_place_in_recep-Tomato-None-Fridge-14/trial_T20190908_091707_240737/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_and_place_simple-SprayBottle-None-GarbageCan-409/trial_T20190908_054803_198732/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_and_place_simple-SprayBottle-None-GarbageCan-409/trial_T20190908_054803_198732/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl"... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_two_obj_and_place-Pen-None-SideTable-329/trial_T20190906_203209_061579/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_two_obj_and_place-Pen-None-SideTable-329/trial_T20190906_203209_061579/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"cur... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Cloth-None-CounterTop-409/trial_T20190908_150707_250921/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Cloth-None-CounterTop-409/trial_T20190908_150707_250921/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_and_place_simple-AlarmClock-None-Shelf-320/trial_T20190907_121126_621870/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_and_place_simple-AlarmClock-None-Shelf-320/trial_T20190907_121126_621870/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"c... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_and_place_simple-SprayBottle-None-Toilet-411/trial_T20190909_110956_758459/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_and_place_simple-SprayBottle-None-Toilet-411/trial_T20190909_110956_758459/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_and_place_simple-CD-None-Dresser-318/trial_T20190907_190229_164232/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_and_place_simple-CD-None-Dresser-318/trial_T20190907_190229_164232/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"curricu... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_two_obj_and_place-ToiletPaper-None-SideTable-420/trial_T20190909_114817_260394/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_two_obj_and_place-ToiletPaper-None-SideTable-420/trial_T20190909_114817_260394/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl"... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_two_obj_and_place-ToiletPaper-None-Toilet-415/trial_T20190908_080148_528030/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_two_obj_and_place-ToiletPaper-None-Toilet-415/trial_T20190908_080148_528030/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_cool_then_place_in_recep-Bread-None-CounterTop-25/trial_T20190906_203227_781169/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_cool_then_place_in_recep-Bread-None-CounterTop-25/trial_T20190906_203227_781169/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_heat_then_place_in_recep-Plate-None-CounterTop-28/trial_T20190907_180330_211175/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_heat_then_place_in_recep-Plate-None-CounterTop-28/trial_T20190907_180330_211175/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_two_obj_and_place-SprayBottle-None-GarbageCan-423/trial_T20190909_013120_095952/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_two_obj_and_place-SprayBottle-None-GarbageCan-423/trial_T20190909_013120_095952/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Pan-None-CounterTop-13/trial_T20190908_113205_515477/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Pan-None-CounterTop-13/trial_T20190908_113205_515477/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl"... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_and_place_simple-KeyChain-None-Dresser-217/trial_T20190910_203316_646156/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_and_place_simple-KeyChain-None-Dresser-217/trial_T20190910_203316_646156/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"c... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Pan-None-StoveBurner-26/trial_T20190909_042547_767945/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Pan-None-StoveBurner-26/trial_T20190909_042547_767945/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Lettuce-None-GarbageCan-20/trial_T20190909_033324_286989/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_clean_then_place_in_recep-Lettuce-None-GarbageCan-20/trial_T20190909_033324_286989/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": ... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_and_place_simple-SaltShaker-None-DiningTable-26/trial_T20190907_113736_672101/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_and_place_simple-SaltShaker-None-DiningTable-26/trial_T20190907_113736_672101/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_two_obj_and_place-AlarmClock-None-Desk-307/trial_T20190907_013752_725369/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_two_obj_and_place-AlarmClock-None-Desk-307/trial_T20190907_013752_725369/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"c... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_and_place_simple-WineBottle-None-DiningTable-15/trial_T20190906_184006_967003/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_and_place_simple-WineBottle-None-DiningTable-15/trial_T20190906_184006_967003/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_two_obj_and_place-Candle-None-Toilet-417/trial_T20190907_182724_868283/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_two_obj_and_place-Candle-None-Toilet-417/trial_T20190907_182724_868283/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"cur... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_two_obj_and_place-Bowl-None-CoffeeTable-203/trial_T20190907_153332_888821/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_two_obj_and_place-Bowl-None-CoffeeTable-203/trial_T20190907_153332_888821/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_heat_then_place_in_recep-Potato-None-Fridge-2/trial_T20190909_030845_198194/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_heat_then_place_in_recep-Potato-None-Fridge-2/trial_T20190909_030845_198194/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/look_at_obj_in_light-TissueBox-None-DeskLamp-301/trial_T20190908_011302_767722/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/look_at_obj_in_light-TissueBox-None-DeskLamp-301/trial_T20190908_011302_767722/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
"... |
You are an expert agent operating in the ALFRED Embodied Environment. Complete the household task by interacting with the environment. At each step, first reason step-by-step within <think> </think> tags, then choose exactly one admissible action and put it within <action> </action> tags. | alf-data/json_2.1.1/train/pick_heat_then_place_in_recep-Mug-None-Cabinet-20/trial_T20190908_230106_156334/game.tw-pddl | {
"env_name": "alfworld",
"env_input": "alf-data/json_2.1.1/train/pick_heat_then_place_in_recep-Mug-None-Cabinet-20/trial_T20190908_230106_156334/game.tw-pddl",
"expert_actions": [],
"workflow_args": "{\"mode\": \"rl\", \"curriculum\": \"none\", \"max_env_steps\": 30}",
"max_env_steps": 30,
"mode": "rl",
... |
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