Metadata-Version: 2.1
Name: real-robots
Version: 0.1.2
Summary: Robots that learn to interact with the environment autonomously
Home-page: https://github.com/spMohanty/real_robots
Author: S.P. Mohanty
Author-email: mohanty@aicrowd.com
License: MIT license
Description: # real-robots
        
        <TABLE " width="100%" BORDER="0">
        <TR>
        <TD><img src="docs/figs/demo0.gif" alt="demo0" width="100%"></TD>
        <TD><img src="docs/figs/demo1.gif" alt="demo1" width="100%"></TD>
        <TD><img src="docs/figs/demo2.gif" alt="demo1" width="100%"></TD>
        </TR>
        </TABLE>
        
        Robots that learn to interact with the environment autonomously
        
        ## Installation
        
        ```bash
        pip install -U real_robots
        ```
        
        ## Usage
        
        ```python
        import gym
        import numpy as np
        import time
        import real_robots
        
        class RandomPolicy:
            def __init__(self, action_space):
                self.action_space = action_space
                self.action = np.zeros(action_space.shape[0])
                self.action += -np.pi*0.5
        
            def step(self, observation, reward, done):
                self.action += 0.4*np.pi*np.random.randn(self.action_space.shape[0])
                return self.action
        
        env = gym.make("REALRobot-v0")
        pi = RandomPolicy(env.action_space)
        env.render("human")
        
        observation = env.reset()
        reward, done = 0, False
        for t in range(40):
            time.sleep(1./1000.)
            action = pi.step(observation, reward, done)
            observation, reward, done, info = env.step(action)
            print(t, reward)
        ```
        
        ## Local Evaluation
        
        ```python
        import gym
        import numpy as np
        import real_robots
        
        class RandomPolicy:
            def __init__(self, action_space):
                self.action_space = action_space
                self.action = np.zeros(action_space.shape[0])
                self.action += -np.pi*0.5
        
            def step(self, observation, reward, done):
                self.action += 0.4*np.pi*np.random.randn(self.action_space.shape[0])
                return self.action
        
        result = real_robots.evaluate(
                        RandomPolicy,
                        intrinsic_timesteps=40,
                        extrinsic_timesteps=40,
                        extrinsic_trials=5,
                        visualize=True,
                        goals_dataset_path="./goals.npy.npz",
                    )
        #  NOTE : You can find a sample goals.npy.npz file at
        #
        #  https://aicrowd-production.s3.eu-central-1.amazonaws.com/misc/REAL-Robots/goals.npy.npz
        print(result)
        # {'score_2D': 0.6949320310408206, 'score_2.5D': 0, 'score_3D': 0, 'score_total': 0.23164401034694018}
        ```
        
        -   Free software: MIT license
        
        ## Features
        
        -   TODO
        
        ## Authors
        
        -   Francesco Mannella
        -   Emilio Cartoni
        -   Sharada Mohanty
        
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
