update reference scores

pen/cloned-v2/data/metadata.json

@@ -1 +1 @@
-{"env_spec": "{\"id\": \"AdroitHandPen-v1\", \"entry_point\": \"gymnasium_robotics.envs.adroit_hand.adroit_pen:AdroitHandPenEnv\", \"reward_threshold\": null, \"nondeterministic\": false, \"max_episode_steps\": 200, \"order_enforce\": true, \"disable_env_checker\": false, \"kwargs\": {\"reward_type\": \"dense\"}, \"additional_wrappers\": [], \"vector_entry_point\": null}", "dataset_id": "D4RL/pen/cloned-v2", "author": ["Rodrigo de Lazcano"], "author_email": ["[email protected]"], "code_permalink": "https://github.com/rodrigodelazcano/d4rl-minari-dataset-generation", "minari_version": "0.4.3", "ref_max_score": 3209.684326171875, "ref_min_score": -209.25643920898438, "num_episodes_average_score": 100, "total_episodes": 3736, "total_steps": 500000, "data_format": "hdf5", "dataset_size": 313.6, "description": "Data obtained by training an imitation policy on the demonstrations from `expert` and `human`, then running the policy, and mixing data at a 50-50 ratio with the demonstrations. This dataset is provided by [D4RL](https://github.com/Farama-Foundation/D4RL/wiki/Tasks#adroit). The environment used to collect the dataset is [`AdroitHandPen-v1`](https://robotics.farama.org/envs/adroit_hand/adroit_pen/).", "action_space": "{\"type\": \"Box\", \"dtype\": \"float32\", \"shape\": [24], \"low\": [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0], \"high\": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}", "observation_space": "{\"type\": \"Box\", \"dtype\": \"float64\", \"shape\": [45], \"low\": [-Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity], \"high\": [Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity]}", "requirements": ["gymnasium-robotics>=1.2.3"]}
+{"env_spec": "{\"id\": \"AdroitHandPen-v1\", \"entry_point\": \"gymnasium_robotics.envs.adroit_hand.adroit_pen:AdroitHandPenEnv\", \"reward_threshold\": null, \"nondeterministic\": false, \"max_episode_steps\": 200, \"order_enforce\": true, \"disable_env_checker\": false, \"kwargs\": {\"reward_type\": \"dense\"}, \"additional_wrappers\": [], \"vector_entry_point\": null}", "dataset_id": "D4RL/pen/cloned-v2", "author": ["Rodrigo de Lazcano"], "author_email": ["[email protected]"], "code_permalink": "https://github.com/rodrigodelazcano/d4rl-minari-dataset-generation", "minari_version": "0.4.3", "ref_max_score": 8820.50845967583, "ref_min_score": 137.92955108500217, "num_episodes_average_score": 100, "total_episodes": 3736, "total_steps": 500000, "data_format": "hdf5", "dataset_size": 313.6, "description": "Data obtained by training an imitation policy on the demonstrations from `expert` and `human`, then running the policy, and mixing data at a 50-50 ratio with the demonstrations. This dataset is provided by [D4RL](https://github.com/Farama-Foundation/D4RL/wiki/Tasks#adroit). The environment used to collect the dataset is [`AdroitHandPen-v1`](https://robotics.farama.org/envs/adroit_hand/adroit_pen/).", "action_space": "{\"type\": \"Box\", \"dtype\": \"float32\", \"shape\": [24], \"low\": [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0], \"high\": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}", "observation_space": "{\"type\": \"Box\", \"dtype\": \"float64\", \"shape\": [45], \"low\": [-Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity], \"high\": [Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity]}", "requirements": ["gymnasium-robotics>=1.2.3"]}