Update README.md

README.md

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 Since we performed several thousand runs on the benchmark to find meaningful HPO test settings in RL, we collect them in this dataset for future use.
 These runs could be used to meta-learn information about the hyperparameter landscape or warmstart HPO tools.
 
-In detail, it contains each 10 runs for PPO, DQN and SAC respectively on the Atari-5 environments, four XLand gridworlds, four Brax walkers, five classic control and two Box2D environments.
+In detail, it contains each 10 runs for the landscape data of PPO, DQN and SAC respectively on the Atari-5 environments, four XLand gridworlds, four Brax walkers, five classic control and two Box2D environments.
+Additionally, it contains each 3 runs for the 5 optimzation algorithms PBT, SMAC, SMAC with Multi-Fidelity and Random Search for each algorithm and environment pair. 
+The dataset follows the mapping: $$\text{Training Budget and Seed, Hyperparameter Configuration} \mapsto \text{Training Performance}$$.
+For the optimization runs, it additionally includes the key *optimization seed* to distinguish configurations between the 5 optimization runs.
 For more information, refer to the [ARLBench](https://arxiv.org/abs/2409.18827) paper.