
 
Decision Transformer
	
Overview
	
The Decision Transformer model was proposed in Decision Transformer: Reinforcement Learning via Sequence Modeling
by Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch.
The abstract from the paper is the following:
We introduce a framework that abstracts Reinforcement Learning (RL) as a sequence modeling problem.
This allows us to draw upon the simplicity and scalability of the Transformer architecture, and associated advances
in language modeling such as GPT-x and BERT. In particular, we present Decision Transformer, an architecture that
casts the problem of RL as conditional sequence modeling. Unlike prior approaches to RL that fit value functions or
compute policy gradients, Decision Transformer simply outputs the optimal actions by leveraging a causally masked
Transformer. By conditioning an autoregressive model on the desired return (reward), past states, and actions, our
Decision Transformer model can generate future actions that achieve the desired return. Despite its simplicity,
Decision Transformer matches or exceeds the performance of state-of-the-art model-free offline RL baselines on
Atari, OpenAI Gym, and Key-to-Door tasks.
Tips:
This version of the model is for tasks where the state is a vector, image-based states will come soon.
This model was contributed by edbeeching. The original code can be found here.
DecisionTransformerConfig
	
class transformers.DecisionTransformerConfig
<
source
>
(
state_dim = 17
act_dim = 4
hidden_size = 128
max_ep_len = 4096
action_tanh = True
vocab_size = 1
n_positions = 1024
n_layer = 3
n_head = 1
n_inner = None
activation_function = 'relu'
resid_pdrop = 0.1
embd_pdrop = 0.1
attn_pdrop = 0.1
layer_norm_epsilon = 1e-05
initializer_range = 0.02
scale_attn_weights = True
use_cache = True
bos_token_id = 50256
eos_token_id = 50256
scale_attn_by_inverse_layer_idx = False
reorder_and_upcast_attn = False
**kwargs
)
Parameters 
state_dim (int, optional, defaults to 17) —
The state size for the RL environment
act_dim (int, optional, defaults to 4) —
The size of the output action space
hidden_size (int, optional, defaults to 128) —
The size of the hidden layers
max_ep_len (int, optional, defaults to 4096) —
The maximum length of an episode in the environment
action_tanh (bool, optional, defaults to True) —
Whether to use a tanh activation on action prediction
vocab_size (int, optional, defaults to 50257) —
Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the
inputs_ids passed when calling DecisionTransformerModel.
n_positions (int, optional, defaults to 1024) —
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
n_layer (int, optional, defaults to 3) —
Number of hidden layers in the Transformer encoder.
n_head (int, optional, defaults to 1) —
Number of attention heads for each attention layer in the Transformer encoder.
n_inner (int, optional) —
Dimensionality of the inner feed-forward layers. If unset, will default to 4 times n_embd.
activation_function (str, optional, defaults to "gelu") —
Activation function, to be selected in the list ["relu", "silu", "gelu", "tanh", "gelu_new"].
resid_pdrop (float, optional, defaults to 0.1) —
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
embd_pdrop (int, optional, defaults to 0.1) —
The dropout ratio for the embeddings.
attn_pdrop (float, optional, defaults to 0.1) —
The dropout ratio for the attention.
layer_norm_epsilon (float, optional, defaults to 1e-5) —
The epsilon to use in the layer normalization layers.
initializer_range (float, optional, defaults to 0.02) —
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
scale_attn_weights (bool, optional, defaults to True) —
Scale attention weights by dividing by sqrt(hidden_size)..
use_cache (bool, optional, defaults to True) —
Whether or not the model should return the last key/values attentions (not used by all models).
scale_attn_by_inverse_layer_idx (bool, optional, defaults to False) —
Whether to additionally scale attention weights by 1 / layer_idx + 1.
reorder_and_upcast_attn (bool, optional, defaults to False) —
Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention
dot-product/softmax to float() when training with mixed precision.
This is the configuration class to store the configuration of a DecisionTransformerModel. It is used to
instantiate a Decision Transformer model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of the standard
DecisionTransformer architecture. Many of the config options are used to instatiate the GPT2 model that is used as
part of the architecture.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the
documentation from PretrainedConfig for more information.
Example:
	Copied
 from transformers import DecisionTransformerConfig, DecisionTransformerModel
 # Initializing a DecisionTransformer configuration
 configuration = DecisionTransformerConfig()
 # Initializing a model (with random weights) from the configuration
 model = DecisionTransformerModel(configuration)
 # Accessing the model configuration
 configuration = model.config
DecisionTransformerGPT2Model
	
class transformers.DecisionTransformerGPT2Model
<
source
>
(
config
)
forward
<
source
>
(
input_ids: typing.Optional[torch.LongTensor] = None
past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None
attention_mask: typing.Optional[torch.FloatTensor] = None
token_type_ids: typing.Optional[torch.LongTensor] = None
position_ids: typing.Optional[torch.LongTensor] = None
head_mask: typing.Optional[torch.FloatTensor] = None
inputs_embeds: typing.Optional[torch.FloatTensor] = None
encoder_hidden_states: typing.Optional[torch.Tensor] = None
encoder_attention_mask: typing.Optional[torch.FloatTensor] = None
use_cache: typing.Optional[bool] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
DecisionTransformerModel
	
class transformers.DecisionTransformerModel
<
source
>
(
config
)
Parameters 
config (~DecisionTransformerConfig) — Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the from_pretrained() method to load the model weights.
The Decision Transformer Model
This model is a PyTorch torch.nn.Module sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
The model builds upon the GPT2 architecture to perform autoregressive prediction of actions in an offline RL
setting. Refer to the paper for more details: https://arxiv.org/abs/2106.01345
forward
<
source
>
(
states = None
actions = None
rewards = None
returns_to_go = None
timesteps = None
attention_mask = None
output_hidden_states = None
output_attentions = None
return_dict = None
)
→
transformers.models.decision_transformer.modeling_decision_transformer.DecisionTransformerOutput or tuple(torch.FloatTensor)
Parameters 
states (torch.FloatTensor of shape (batch_size, episode_length, state_dim)) —
The states for each step in the trajectory
actions (torch.FloatTensor of shape (batch_size, episode_length, act_dim)) —
The actions taken by the “expert” policy for the current state, these are masked for auto regressive
prediction
rewards (torch.FloatTensor of shape (batch_size, episode_length, 1)) —
The rewards for each state, action
returns_to_go (torch.FloatTensor of shape (batch_size, episode_length, 1)) —
The returns for each state in the trajectory
timesteps (torch.LongTensor of shape (batch_size, episode_length)) —
The timestep for each step in the trajectory
attention_mask (torch.LongTensor of shape (batch_size, episode_length)) —
Masking, used to mask the actions when performing autoregressive prediction
Returns
transformers.models.decision_transformer.modeling_decision_transformer.DecisionTransformerOutput or tuple(torch.FloatTensor)
A transformers.models.decision_transformer.modeling_decision_transformer.DecisionTransformerOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (DecisionTransformerConfig) and inputs.
last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.
state_preds (torch.FloatTensor of shape (batch_size, sequence_length, state_dim)) — Environment state predictions
action_preds (torch.FloatTensor of shape (batch_size, sequence_length, action_dim)) — Model action predictions
return_preds (torch.FloatTensor of shape (batch_size, sequence_length, 1)) — Predicted returns for each state
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The DecisionTransformerModel forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Examples:
	Copied
 from transformers import DecisionTransformerModel
 import torch
 model = DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-medium")
 # evaluation
 model = model.to(device)
 model.eval()
 env = gym.make("Hopper-v3")
 state_dim = env.observation_space.shape[0]
 act_dim = env.action_space.shape[0]
 state = env.reset()
 states = torch.from_numpy(state).reshape(1, 1, state_dim).to(device=device, dtype=torch.float32)
 actions = torch.zeros((1, 1, act_dim), device=device, dtype=torch.float32)
 rewards = torch.zeros(1, 1, device=device, dtype=torch.float32)
 target_return = torch.tensor(TARGET_RETURN, dtype=torch.float32).reshape(1, 1)
 timesteps = torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
 attention_mask = torch.zeros(1, 1, device=device, dtype=torch.float32)
 # forward pass
 with torch.no_grad():
...     state_preds, action_preds, return_preds = model(
...         states=states,
...         actions=actions,
...         rewards=rewards,
...         returns_to_go=target_return,
...         timesteps=timesteps,
...         attention_mask=attention_mask,
...         return_dict=False,
...     )
←X-CLIP
Trajectory Transformer→
Decision Transformer
Overview
DecisionTransformerConfig
DecisionTransformerGPT2Model
DecisionTransformerModel
