rustyshackleforth/rocca

Rational OpenCog Controlled Agent (ROCCA). Use OpenCog to control a rational agent in OpenAI Gym and Malmo environments.

Rational OpenCog Controlled Agent

Description

Rational OpenCog Controlled Agent, or ROCCA, is a project aiming at creating an opencog agent that acts rationally in OpenAI Gym environments (including Minecraft via minerl).

At its core it relies on PLN (Probabilistic Logic Networks) for both learning and planning. In practice most of the learning is however handled by the pattern miner, which can be seen as a specialized form of PLN reasoning. Planning, the discovery of cognitive schematics, is handled by PLN and its temporal reasoning rule base. Decision is currently a hardwired module, heavily inspired by OpenPsi with a more rational sampling procedure (Thompson Sampling for better exploitation vs exploration tradeoff).

Status

For now learning is only able to abstract temporal patterns based on directly observable events. That is the agent is able to notice that particular action sequences in certain contexts tend to be followed by rewards, however it is not, as of right now, able to reason about action sequences that it has never observed. This requires Temporal Deduction, currently under development.

Once Temporal Deduction is complete we still have a lot of things to add such as

1. More sophisticated temporal and then spatial inference rules.
2. ECAN, for Attention Allocation, to dynamically restrict the atomspace to subsets of items to process/pay-attention-to.
3. Record attention spreading to learn/improve Hebbian links.
4. Concept creation and schematization (crystallized attention allocation).
5. Record internal processes, not just attention spreading, as percepta to enable deeper forms of instrospective reasoning.
6. Plan internal actions, not just external, to enable self-growth.

Requirements

OpenCog tools

• cogutil
• atomspace
• ure
• spacetime
• pln
• miner
• [optional] cogserver
• [optional] attention
• [optional] opencog

Third party tools

• Python 3
• python-orderedmultidict https://pypi.org/project/orderedmultidict/
• fastcore https://fastcore.fast.ai
• OpenAI Gym https://gym.openai.com/
• MineRL https://minerl.io
• nbdev https://nbdev.fast.ai

Install

In the root folder enter the following command:

pip install -e .

How to use

A gym agent defined under the rocca/agents folder is provided that can used to implement agents for given environments. See the examples under the examples folder.

There are Jupyter notebooks provided for experimentation as well.