Metadata-Version: 2.1
Name: replay-monitor
Version: 0.0.3
Summary: A tool for easy data exploration in reinforcement learning environments.
Home-page: https://github.com/liorcohen5/replay-monitor
Author: Leor Cohen
Author-email: liorcohen5@gmail.com
License: UNKNOWN
Download-URL: https://github.com/liorcohen5/replay-monitor/archive/0.0.3.tar.gz
Description: #Replay Monitor
        
        
        This is a tool for recording and observing data and measurements generated through the interactions between 
        a reinforcement learning algorithm and an environment with an OpenAI Gym interface.
        
        Currently, this tool offers two main features:
        *  A convenient environment wrapper that allows the user to:
            *  Record Tensorboard metrics during the training of the RL agent
            *  Log the entire interaction with the environment in a local DB (for later use with the interactive tool below).
        
        * An interactive tool that visualize stored interactions (episodes and transitions) on-demand.
        
        This tool supports complex state spaces, including tuple spaces.
        
        Note: This is a premature release, keep in mind that since this package is still in development, bugs and changes 
        are expected.
        
        ## Installation
        Install the package by
        ```
        pip install replay-monitor
        ```
        
        ## Usage Examples
        ### Record Agent Interactions
        To use the environment wrapper for storing interactions:
        ```python
        from replay_monitor import Monitor
        import gym
        
        env = gym.make('Breakout-v0')
        env = Monitor(env, log_to_db=True)
        ```
        Now, you can use the environment as usual, for example:
        ```
        env.reset()
        for i in range(300):
            action = env.action_space.sample()
            state, reward, done, info = env.step(action)
        
            if done:
                env.reset()
        ...
        ```
        ### Use The Interactive Tool
        Run the interactive tool by executing the following command in the command-line 
        (make sure your environment is activated if you use virtualenv):
        ```
        replay-monitor --db_path <db_path>
        ```
        where `<db_path>` is the path to the .h5 file generated by the environment wrapper `Monitor` 
        (you can omit `--db_path` if you use the default value).
        
        ### Record Tensorboard Metrics
        TODO
Keywords: reinforcement learning,tool,data exploration,replay,monitor,analytical tool
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
