# AgentFlow: A Modular Toolkit for Scalable RL Research
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## Overview
`AgentFlow` is a library for composing Reinforcement-Learning agents. The core
features that AgentFlow provides are:
1. tools for slicing, transforming, and composing *specs*
2. 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.
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### [Components](docs/components.md)
### [Control Flow](docs/control_flow.md)
### [Examples](docs/examples.md)
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"description": "# AgentFlow: A Modular Toolkit for Scalable RL Research\n\n<!--* B 2021-07-21 internal placeholder *-->\n\n## Overview\n\n`AgentFlow` is a library for composing Reinforcement-Learning agents. The core\nfeatures that AgentFlow provides are:\n\n1. tools for slicing, transforming, and composing *specs*\n2. tools for encapsulating and composing RL-tasks.\n\nUnlike the standard RL setup, which assumes a single environment and an agent,\n`AgentFlow` is designed for the single-embodiment, multiple-task regime. This\nwas motivated by the robotics use-case, which frequently requires training RL\nmodules for various skills, and then composing them (possibly with non-learned\ncontrollers too).\n\nInstead of having to implement a separate RL environment for each skill and\ncombine them ad hoc, with `AgentFlow` you can define one or more `SubTasks`\nwhich *modify* a timestep from a single top-level environment, e.g. adding\nobservations and defining rewards, or isolating a particular sub-system of the\nenvironment, such as a robot arm.\n\nYou then *compose* SubTasks with regular RL-agents to form modules, and use a\nset of graph-building operators to define the flow of these modules over time\n(hence the name `AgentFlow`).\n\nThe graph-building step is entirely optional, and is intended only for use-cases\nthat require something like a (possibly learnable, possibly stochastic)\nstate-machine.\n\n<!-- Internal placeholder C -->\n### [Components](docs/components.md)\n### [Control Flow](docs/control_flow.md)\n### [Examples](docs/examples.md)\n<!-- Internal placeholder D -->\n",
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