A tool for easy data exploration in reinforcement learning environments.
Project 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:
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
Project details
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