Metadata-Version: 2.1
Name: real_robots
Version: 0.1.20
Summary: Robots that learn to interact with the environment autonomously
Home-page: https://github.com/aicrowd/real_robots
Author: S.P. Mohanty
Author-email: mohanty@aicrowd.com
License: MIT license
Description: # real-robots
        
        ![https://travis-ci.com/AIcrowd/real_robots.svg?branch=master](https://travis-ci.com/AIcrowd/real_robots.svg?branch=master)
        
        <TABLE " width="100%" BORDER="0">
        <TR>
        <TD><img src="https://i.imgur.com/ORXaKBB.gif" alt="demo0" width="100%"></TD>
        <TD><img src="https://i.imgur.com/w66lz4L.gif" alt="demo1" width="100%"></TD>
        <TD><img src="https://i.imgur.com/oYARyZV.gif" alt="demo1" width="100%"></TD>
        </TR>
        </TABLE>
        
        Robots that learn to interact with the environment autonomously
        
        ## Installation
        
        ```bash
        pip install -U real_robots
        ```
        
        If everything went well, then you should be able to run :
        
        ```
        real-robots-demo
        ```
        
        and it should (eventually) open up a small window with a little robotic arm
        doing random stuff.
        
        ## Usage
        
        ```python
        import gym
        import numpy as np
        import time
        import real_robots
        from real_robots.policy import BasePolicy
        
        class RandomPolicy(BasePolicy):
            def __init__(self, action_space):
                self.action_space = action_space
                self.action = action_space.sample()
        
            def step(self, observation, reward, done):
                if np.random.rand() < 0.05:
                    self.action = self.action_space.sample()
                return self.action
        
        env = gym.make("REALRobot2020-R2J3-v0")
        pi = RandomPolicy(env.action_space)
        env.render("human")
        
        observation = env.reset()
        reward, done = 0, False
        for t in range(40):    
            action = pi.step(observation, reward, done)
            observation, reward, done, info = env.step(action)    
        ```
        
        ## Local Evaluation
        
        ```python
        import gym
        import numpy as np
        import real_robots
        from real_robots.policy import BasePolicy
        
        class RandomPolicy(BasePolicy):
            def __init__(self, action_space):
                self.action_space = action_space
                self.action = action_space.sample()
        
            def step(self, observation, reward, done):
                if np.random.rand() < 0.05:
                    self.action = self.action_space.sample()
                return self.action
        
        result, detailed_scores = real_robots.evaluate(
                        RandomPolicy,
                        environment='R1',
                        action_type='macro_action',
                        n_objects=1,
                        intrinsic_timesteps=1e3,
                        extrinsic_timesteps=1e3,
                        extrinsic_trials=3,
                        visualize=False,
                        goals_dataset_path='goals-REAL2020-s2020-50-1.npy.npz'
                    )
        # NOTE : You can find goals-REAL2020-s2020-50-1.npy.npz file in the REAL2020 Starter Kit repository
        # or you can generate one using the real-robots-generate-goals command.
        #
        print(result)
        # {'score_REAL2020': 0.06529471503519801, 'score_total': 0.06529471503519801}
        print(detailed_scores)
        # {'REAL2020': [0.00024387094790936833, 0.19553060745741896, 0.00010966670026571288]}
        ```
        
        See also our [FAQ](FAQ.md).
        
        -   Free software: MIT license
        
        ## Features
        
        The REALRobot environment is a standard gym environment.  
        It includes a 7DoF kuka arm with a 2DoF gripper, a table with 3 objects on it and a camera looking at the table from the top. 
        For more info on the environment see [environment.md](environment.md).
        
        ## Authors
        
        -   Francesco Mannella
        -   Emilio Cartoni
        -   **[Sharada Mohanty](https://twitter.com/MeMohanty)**
        
Keywords: real_robots
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
