usage example did not work because of typo and torch not imported

README.md

@@ -1,45 +1,45 @@
----
-tags:
-- deep-reinforcement-learning
-- reinforcement-learning
-
----
-
-Find here pretrained model weights for the [Decision Transformer] (https://github.com/kzl/decision-transformer).
-Weights are available for 4 Atari games: Breakout, Pong, Qbert and Seaquest. Found in the checkpoints directory.
-We share models trained for one seed (123), whereas the paper contained weights for 3 random seeds.
-
-
-### Usage
-
-```
-git clone https://huggingface.co/edbeeching/decision_transformer_atari
-conda env create -f conda_env.yml
-```
-
-Then, you can use the model like this:
-
-```python
-
-from decision_transform_atari import GPTConfig, GPT
-
-vocab_size = 4
-block_size = 90
-model_type = "reward_conditioned"
-timesteps = 2654
-
-mconf = GPTConfig(
-    vocab_size,
-    block_size,
-    n_layer=6,
-    n_head=8,
-    n_embd=128,
-    model_type=model_type,
-    max_timestep=timesteps,
-)
-model = GPT(mconf)
-
-checkpoint_path = "checkpoints/Breakout_123.pth"  # or Pong, Qbert, Seaquest
-checkpoint = torch.load(checkpoint_path)
-model.load_state_dict(checkpoint)
-```
+---
+tags:
+- deep-reinforcement-learning
+- reinforcement-learning
+
+---
+
+Find here pretrained model weights for the [Decision Transformer] (https://github.com/kzl/decision-transformer).
+Weights are available for 4 Atari games: Breakout, Pong, Qbert and Seaquest. Found in the checkpoints directory.
+We share models trained for one seed (123), whereas the paper contained weights for 3 random seeds.
+
+
+### Usage
+
+```
+git clone https://huggingface.co/edbeeching/decision_transformer_atari
+conda env create -f conda_env.yml
+```
+
+Then, you can use the model like this:
+
+```python
+import torch
+from decision_transformer_atari import GPTConfig, GPT
+
+vocab_size = 4
+block_size = 90
+model_type = "reward_conditioned"
+timesteps = 2654
+
+mconf = GPTConfig(
+    vocab_size,
+    block_size,
+    n_layer=6,
+    n_head=8,
+    n_embd=128,
+    model_type=model_type,
+    max_timestep=timesteps,
+)
+model = GPT(mconf)
+
+checkpoint_path = "checkpoints/Breakout_123.pth"  # or Pong, Qbert, Seaquest
+checkpoint = torch.load(checkpoint_path)
+model.load_state_dict(checkpoint)
+```