Only released in EOL distros:
Package Summary
rl_msgs is a package of ROS message definitions, which are used for a reinforcement learning agent to communicate with an environment.
- Maintainer: Todd Hester <todd.hester AT gmail DOT com>
- Author: Michael Quinlan, Todd Hester
- License: BSD
- Source: git https://github.com/toddhester/rl-texplore-ros-pkg.git (branch: master)
Contents
Documentation
This package defines a standard way for agents and environments to communicate through ROS messages. In ROS computation is done by nodes that perform different tasks that communicate through a set of ROS messages. ROS provides the framework to publish, subscribe to and receive these messages, in addition to tools for logging, displaying, plotting, and playing back ROS messages. This package defines a set of ROS messages for a reinforcement learning agent and environment to communicate. These messages are similar to the messages used by RL-Glue (Tanner and White 2009), but are simplified and defined in the ROS format.
Please take a look at the tutorial on how to install, compile, and use this package
Check out the code at: https://github.com/toddhester/rl-texplore-ros-pkg
Agents from the rl_agent package can communicate with environments from the rl_env package through this interface. In addition, you can write your own agents and environments that will work with the ones in those packages by passing the same messages defined here.
The framework is being designed to be as flexible as possible. While our algorithms and environments will mostly follow a certain style, we understand that every robot and every experiment differs. Our hope is to not force our philosophy onto other people, as long as you adhere to rl_msgs you can design and implement your algorithms and environments in your own style.
Messages passed from Agent to Environment
RLAction: This message sends the environment the action (an integer) that the agent has selected. rl_msgs/RLAction
RLExperimentInfo: This message provides information on the results of the latest episode of the experiment, namely an episode number, the sum of rewards for that episode, and the number of actions the agent took in that episode. The episode_reward can be plotted to look at rewards per episode. rl_msgs/RLExperimentInfo
Messages passed from Environment to Agent
RLEnvDescription: This message describes the environment with a title, the number of actions, the number of states, the range of each state feature, the maximum one-step reward, the range of rewards, if the task is episodic or not, and if the task is deterministic or stochastic. rl_msgs/RLEnvDescription
RLEnvSeedExperience: This message provides a full experience <s, a, s', r> seed for the agent to use for learning. In addition to the state, action, next state, and reward, it also includes a boolean indicating if it is a terminal transition or not. rl_msgs/RLEnvSeedExperience
RLStateReward: This is a message from the environment with the agent's new state, the reward received on this time step, and whether this transition is terminal or not. rl_msgs/RLStateReward
