Update README.md

README.md

@@ -50,6 +50,22 @@ size_categories:
 # Procgen Benchmark - Bigfish
 This dataset contains trajectories generated by a [PPO](https://arxiv.org/abs/1707.06347) reinforcement learning agent trained on the Bigfish environment from the [Procgen Benchmark](https://openai.com/index/procgen-benchmark/). The agent has been trained for 50M steps and the final evaluation performance is `32.33`.
 
+## Dataset Usage
+
+Regular usage:
+```python
+from datasets import load_dataset
+train_dataset = load_dataset("EpicPinkPenguin/procgen_bigfish", split="train")
+test_dataset = load_dataset("EpicPinkPenguin/procgen_bigfish", split="test")
+```
+
+Usage with PyTorch:
+```python
+from datasets import load_dataset
+train_dataset = load_dataset("EpicPinkPenguin/procgen_bigfish", split="train").with_format("torch")
+test_dataset = load_dataset("EpicPinkPenguin/procgen_bigfish", split="test").with_format("torch")
+```
+
 ## Dataset Structure
 ### Data Instances
 Each data instance represents a single step consisting of tuples of the form (observation, action, reward, done, truncated) = (o_t, a_t, r_{t+1}, done_{t+1}, trunc_{t+1}).
@@ -98,7 +114,7 @@ Each data instance represents a single step consisting of tuples of the form (ob
 The dataset is divided into a `train` (90%) and `test` (10%) split
 
 ## Dataset Creation
-The dataset was created by training an RL agent with [PPO](https://arxiv.org/abs/1707.06347) for 50M steps on the Procgen Bigfish environment. The agent obtained a final performance of `32.33`. The trajectories where generated by taking the argmax action at each step, corresponding to taking the mode of the action distribtution.
+The dataset was created by training an RL agent with [PPO](https://arxiv.org/abs/1707.06347) for 50M steps on the Procgen Bigfish environment. The agent obtained a final performance of `32.33`. The trajectories where generated by taking the argmax action at each step, corresponding to taking the mode of the action distribution.
 
 ## Procgen Benchmark
 The [Procgen Benchmark](https://openai.com/index/procgen-benchmark/), released by OpenAI, consists of 16 procedurally-generated environments designed to measure how quickly reinforcement learning (RL) agents learn generalizable skills. It emphasizes experimental convenience, high diversity within and across environments, and is ideal for evaluating both sample efficiency and generalization. The benchmark allows for distinct training and test sets in each environment, making it a standard research platform for the OpenAI RL team. It aims to address the need for more diverse RL benchmarks compared to complex environments like Dota and StarCraft.