AgentFlow is a library for composing Reinforcement-Learning agents. The core
features that AgentFlow provides are:
- tools for slicing, transforming, and composing specs
- tools for encapsulating and composing RL-tasks.
Unlike the standard RL setup, which assumes a single environment and an agent,
AgentFlow is designed for the single-embodiment, multiple-task regime. This
was motivated by the robotics use-case, which frequently requires training RL
modules for various skills, and then composing them (possibly with non-learned
controllers too).
Instead of having to implement a separate RL environment for each skill and
combine them ad hoc, with AgentFlow you can define one or more SubTasks
which modify a timestep from a single top-level environment, e.g. adding
observations and defining rewards, or isolating a particular sub-system of the
environment, such as a robot arm.
You then compose SubTasks with regular RL-agents to form modules, and use a
set of graph-building operators to define the flow of these modules over time
(hence the name AgentFlow).
The graph-building step is entirely optional, and is intended only for use-cases that require something like a (possibly learnable, possibly stochastic) state-machine.