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README.md

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 # The ARLBench Performance Dataset
 
 [ARLBench](https://github.com/automl/arlbench) is a benchmark for hyperparameter optimization in Reinforcement Learning. 
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 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: 
-$$\text{Training Budget and Seed, Hyperparameter Configuration} \mapsto \text{Training Performance}$$.
+$$\text{Training Budget and Seed, Hyperparameter Configuration} \mapsto \text{Training Performance}$$
 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.