Update README.md

README.md

@@ -96,9 +96,6 @@ tags:
 - starpilot
 ---
 # Procgen Benchmark
-
-<video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/brMaX1xgew7ulqkMU0Ahi.mp4"></video>
-
 This dataset contains expert trajectories generated by a [PPO](https://arxiv.org/abs/1707.06347) reinforcement learning agent trained on each of the 16 procedurally-generated gym environments from the [Procgen Benchmark](https://openai.com/index/procgen-benchmark/). The environments were created on `distribution_mode=easy` and with unlimited levels.
 
 Disclaimer: This is not an official repository from OpenAI.
@@ -126,7 +123,7 @@ The PPO RL agent was trained for 50M steps on each environment and obtained the
 |:------------|:-------|
 | bigfish     | 32.77  |
 | bossfight   | 12.49  |
-| caveflyer   | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/brMaX1xgew7ulqkMU0Ahi.mp4"></video>  |
+| caveflyer   | xx.xx  |
 | chaser      | xx.xx  |
 | climber     | xx.xx  |
 | coinrun     | xx.xx  |
@@ -192,5 +189,27 @@ The dataset is divided into a `train` (90%) and `test` (10%) split. Each environ
 ## Dataset Creation
 The dataset was created by training an RL agent with [PPO](https://arxiv.org/abs/1707.06347) for 50M steps in each environment. The trajectories where generated by taking the argmax action at each step, corresponding to taking the mode of the action distribution. Consequently the rollout policy is deterministic. The environments were created on `distribution_mode=easy` and with unlimited levels.
 
+## Video Samples
+Here is a collection of videos with the RGB observations from the dataset.
+
+| Environment | Return |
+|:------------|:-------|
+| bigfish     | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/brMaX1xgew7ulqkMU0Ahi.mp4"></video>  |
+| bossfight   | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/brMaX1xgew7ulqkMU0Ahi.mp4"></video>  |
+| caveflyer   | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/brMaX1xgew7ulqkMU0Ahi.mp4"></video>  |
+| chaser      | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/brMaX1xgew7ulqkMU0Ahi.mp4"></video>  |
+| climber     | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/brMaX1xgew7ulqkMU0Ahi.mp4"></video>  |
+| coinrun     | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/brMaX1xgew7ulqkMU0Ahi.mp4"></video>  |
+| dodgeball   | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/brMaX1xgew7ulqkMU0Ahi.mp4"></video>  |
+| fruitbot    | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/brMaX1xgew7ulqkMU0Ahi.mp4"></video>  |
+| heist       | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/brMaX1xgew7ulqkMU0Ahi.mp4"></video>  |
+| jumper      | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/brMaX1xgew7ulqkMU0Ahi.mp4"></video>  |
+| leaper      | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/brMaX1xgew7ulqkMU0Ahi.mp4"></video>  |
+| maze        | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/brMaX1xgew7ulqkMU0Ahi.mp4"></video>  |
+| miner       | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/brMaX1xgew7ulqkMU0Ahi.mp4"></video>  |
+| ninja       | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/brMaX1xgew7ulqkMU0Ahi.mp4"></video>  |
+| plunder     | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/brMaX1xgew7ulqkMU0Ahi.mp4"></video>  |
+| starpilot   | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/brMaX1xgew7ulqkMU0Ahi.mp4"></video>  |
+
 ## 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.