
# Dataset Description

## Structure

- Consists of 5 fields
- Each row corresponds to a policy - sequence of actions, given an initial `<START>` state, and corresponding rewards at each step.

## Fields

`steps`, `step_attn_masks`, `rewards`, `actions`, `dones`

## Field descriptions

- `steps` (List of lists of `Int`s) - tokenized step tokens of all the steps in the policy sequence (here we use the `roberta-base` tokenizer, as `roberta-base` would be used to encode each step of a recipe)
- `step_attn_masks` (List of lists of `Int`s) - Attention masks corresponding to `steps`
- `rewards` (List of `Float`s) - Sequence of rewards (normalized b/w 0 and 1) assigned per step.
- `actions` (List of lists of `Int`s) - Sequence of actions (one-hot encoded, as the action space is discrete). There are `33` different actions possible (we consider the maximum number of steps per recipe = `16`, so the action can vary from `-16` to `+16`; The class label is got by adding 16 to the actual action value)
- `dones` (List of `Bool`) - Sequence of flags, conveying if the work is completed when that step is reached, or not.

## Dataset Size

- Number of rows = `2255673`
- Maximum number of steps per row = `16`
multilinguality:monolingual language:en region:us
{"annotations_creators": [], "language_creators": [], "language": ["en"], "license": [], "multilinguality": ["monolingual"], "size_categories": [], "source_datasets": [], "task_categories": [], "task_ids": [], "pretty_name": "recipe RL roberta base", "tags": []}
TAGS
#multilinguality-monolingual #language-English #region-us 


# Dataset Description

## Structure

- Consists of 5 fields
- Each row corresponds to a policy - sequence of actions, given an initial '<START>' state, and corresponding rewards at each step.

## Fields

'steps', 'step_attn_masks', 'rewards', 'actions', 'dones'

## Field descriptions

- 'steps' (List of lists of 'Int's) - tokenized step tokens of all the steps in the policy sequence (here we use the 'roberta-base' tokenizer, as 'roberta-base' would be used to encode each step of a recipe)
- 'step_attn_masks' (List of lists of 'Int's) - Attention masks corresponding to 'steps'
- 'rewards' (List of 'Float's) - Sequence of rewards (normalized b/w 0 and 1) assigned per step.
- 'actions' (List of lists of 'Int's) - Sequence of actions (one-hot encoded, as the action space is discrete). There are '33' different actions possible (we consider the maximum number of steps per recipe = '16', so the action can vary from '-16' to '+16'; The class label is got by adding 16 to the actual action value)
- 'dones' (List of 'Bool') - Sequence of flags, conveying if the work is completed when that step is reached, or not.

## Dataset Size

- Number of rows = '2255673'
- Maximum number of steps per row = '16'
# Dataset Description ## Structure

- Consists of 5 fields
- Each row corresponds to a policy - sequence of actions, given an initial '<START>' state, and corresponding rewards at each step. ## Fields

'steps', 'step_attn_masks', 'rewards', 'actions', 'dones' ## Field descriptions

- 'steps' (List of lists of 'Int's) - tokenized step tokens of all the steps in the policy sequence (here we use the 'roberta-base' tokenizer, as 'roberta-base' would be used to encode each step of a recipe)
- 'step_attn_masks' (List of lists of 'Int's) - Attention masks corresponding to 'steps'
- 'rewards' (List of 'Float's) - Sequence of rewards (normalized b/w 0 and 1) assigned per step.
- 'actions' (List of lists of 'Int's) - Sequence of actions (one-hot encoded, as the action space is discrete). There are '33' different actions possible (we consider the maximum number of steps per recipe = '16', so the action can vary from '-16' to '+16'; The class label is got by adding 16 to the actual action value)
- 'dones' (List of 'Bool') - Sequence of flags, conveying if the work is completed when that step is reached, or not. ## Dataset Size

- Number of rows = '2255673'
- Maximum number of steps per row = '16'
TAGS
#multilinguality-monolingual #language-English #region-us 
 # Dataset Description ## Structure

- Consists of 5 fields
- Each row corresponds to a policy - sequence of actions, given an initial '<START>' state, and corresponding rewards at each step. ## Fields

'steps', 'step_attn_masks', 'rewards', 'actions', 'dones' ## Field descriptions

- 'steps' (List of lists of 'Int's) - tokenized step tokens of all the steps in the policy sequence (here we use the 'roberta-base' tokenizer, as 'roberta-base' would be used to encode each step of a recipe)
- 'step_attn_masks' (List of lists of 'Int's) - Attention masks corresponding to 'steps'
- 'rewards' (List of 'Float's) - Sequence of rewards (normalized b/w 0 and 1) assigned per step.
- 'actions' (List of lists of 'Int's) - Sequence of actions (one-hot encoded, as the action space is discrete). There are '33' different actions possible (we consider the maximum number of steps per recipe = '16', so the action can vary from '-16' to '+16'; The class label is got by adding 16 to the actual action value)
- 'dones' (List of 'Bool') - Sequence of flags, conveying if the work is completed when that step is reached, or not. ## Dataset Size

- Number of rows = '2255673'
- Maximum number of steps per row = '16'