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README.md
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data_files:
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- "atari_battle_zone_ppo.csv"
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- "atari_double_dunk_ppo.csv"
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- config_name: dqn_data
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data_files:
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- "atari_battle_zone_dqn.csv"
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- "atari_double_dunk_dqn.csv"
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- config_name: sac_data
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data_files:
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---
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# The ARLBench Performance Dataset
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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.
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These runs could be used to meta-learn information about the hyperparameter landscape or warmstart HPO tools.
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In detail, it contains each 10 runs for PPO, DQN and SAC respectively on the Atari-5 environments, four XLand gridworlds, four
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For more information, refer to the ARLBench paper.
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data_files:
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- "atari_battle_zone_ppo.csv"
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- "atari_double_dunk_ppo.csv"
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- "atari_phoenix_ppo.csv"
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- "atari_qbert_ppo.csv"
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- "atari_this_game_ppo.csv"
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- "box2d_lunar_lander_continuous_ppo.csv"
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- "box2d_lunar_lander_ppo.csv"
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- "brax_halfcheetah_ppo.csv"
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- "brax_hopper_ppo.csv"
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- "brax_ant_ppo.csv"
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- "brax_humanoid_ppo.csv"
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- "cc_acrobot_ppo.csv"
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- "cc_cartpole_ppo.csv"
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- "cc_continuous_mountain_car_ppo.csv"
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- "cc_mountain_car_ppo.csv"
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- "cc_pendulum_ppo.csv"
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- "minigrid_door_key_ppo.csv"
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- "minigrid_empty_random_ppo.csv"
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- "minigrid_four_rooms_ppo.csv"
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- "minigrid_unlock_ppo.csv"
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- config_name: dqn_data
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data_files:
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- "atari_battle_zone_dqn.csv"
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- "atari_double_dunk_dqn.csv"
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- "atari_phoenix_dqn.csv"
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- "atari_qbert_dqn.csv"
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- "atari_this_game_dqn.csv"
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- "minigrid_door_key_dqn.csv"
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- "minigrid_empty_random_dqn.csv"
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- "minigrid_four_rooms_dqn.csv"
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- "minigrid_unlock_dqn.csv"
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- "cc_acrobot_dqn.csv"
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- "cc_cartpole_dqn.csv"
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- "cc_mountain_car_dqn.csv"
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- "box2d_lunar_lander_dqn.csv"
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- config_name: sac_data
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data_files:
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- "box2d_bipedal_walker_sac.csv"
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- "box2d_lunar_lander_continuous_sac.csv"
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- "brax_halfcheetah_sac.csv"
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- "brax_hopper_sac.csv"
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- "brax_ant_sac.csv"
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- "brax_humanoid_sac.csv"
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- "cc_continuous_mountain_car_sac.csv"
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- "cc_pendulum_sac.csv"
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---
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# The ARLBench Performance Dataset
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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.
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These runs could be used to meta-learn information about the hyperparameter landscape or warmstart HPO tools.
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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.
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For more information, refer to the ARLBench paper.
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