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        "author": "S.P. Mohanty",
        "author_email": "mohanty@aicrowd.com",
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        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7"
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        "description": "# real-robots\n\n![https://travis-ci.com/AIcrowd/real_robots.svg?branch=master](https://travis-ci.com/AIcrowd/real_robots.svg?branch=master)\n\n<TABLE \" width=\"100%\" BORDER=\"0\">\n<TR>\n<TD><img src=\"https://i.imgur.com/ORXaKBB.gif\" alt=\"demo0\" width=\"100%\"></TD>\n<TD><img src=\"https://i.imgur.com/w66lz4L.gif\" alt=\"demo1\" width=\"100%\"></TD>\n<TD><img src=\"https://i.imgur.com/oYARyZV.gif\" alt=\"demo1\" width=\"100%\"></TD>\n</TR>\n</TABLE>\n\nRobots that learn to interact with the environment autonomously\n\n## Installation\n\n```bash\npip install -U real_robots\n```\n\nIf everything went well, then you should be able to run :\n\n```\nreal-robots-demo\n```\n\nand it should (eventually) open up a small window with a little robotic arm\ndoing random stuff.\n\n## Usage\n\n```python\nimport gym\nimport numpy as np\nimport time\nimport real_robots\nfrom real_robots.policy import BasePolicy\n\nclass RandomPolicy(BasePolicy):\n    def __init__(self, action_space):\n        self.action_space = action_space\n        self.action = np.zeros(action_space.shape[0])\n        self.action += -np.pi*0.5\n\n    def step(self, observation, reward, done):\n        self.action += 0.4*np.pi*np.random.randn(self.action_space.shape[0])\n        return self.action\n\nenv = gym.make(\"REALRobot-v0\")\npi = RandomPolicy(env.action_space)\nenv.render(\"human\")\n\nobservation = env.reset()\nreward, done = 0, False\nfor t in range(40):\n    time.sleep(1./1000.)\n    action = pi.step(observation, reward, done)\n    observation, reward, done, info = env.step(action)\n    print(t, reward)\n```\n\n## Local Evaluation\n\n```python\nimport gym\nimport numpy as np\nimport real_robots\nfrom real_robots.policy import BasePolicy\n\nclass RandomPolicy(BasePolicy):\n    def __init__(self, action_space):\n        self.action_space = action_space\n        self.action = np.zeros(action_space.shape[0])\n        self.action += -np.pi*0.5\n\n    def step(self, observation, reward, done):\n        self.action += 0.4*np.pi*np.random.randn(self.action_space.shape[0])\n        return self.action\n\nresult, detailed_scores = real_robots.evaluate(\n                RandomPolicy,\n                intrinsic_timesteps=40,\n                extrinsic_timesteps=40,\n                extrinsic_trials=5,\n                visualize=True,\n                goals_dataset_path=\"./goals.npy.npz\",\n            )\n#  NOTE : You can find a sample goals.npy.npz file at\n#\n#  https://aicrowd-production.s3.eu-central-1.amazonaws.com/misc/REAL-Robots/goals.npy.npz\nprint(result)\n# {'score_total': 0.2327459533906755, 'score_2D': 0.6982378601720265, 'score_2.5D': 0, 'score_3D': 0}\nprint(detailed_scores)\n# {'2D': [0.6646365565451159, 0.6632591441787807, 0.7569003923985664, 0.7167885964780916, 0.6896046112595778]}\n```\n\nSee also our [FAQ](https://github.com/AIcrowd/real_robots/blob/master/FAQ.md).\n\n-   Free software: MIT license\n\n## Features\n\n-   TODO\n\n## Authors\n\n-   Francesco Mannella\n-   Emilio Cartoni\n-   **[Sharada Mohanty](https://twitter.com/MeMohanty)**",
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