Inverse-Reinforcement-Learning

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Inverse-Reinforcement-Learning is an open-source AI Agents & Automation tool that implements selected inverse reinforcement learning algorithms. It includes linear programming, maximum entropy, and deep maximum entropy IRL algorithms.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is Inverse-Reinforcement-Learning?

Inverse-Reinforcement-Learning is an open-source project providing implementations of various inverse reinforcement learning (IRL) algorithms. Developed as part of COMP3710, it was supervised by Dr Mayank Daswani and Dr Marcus Hutter. The project includes linear programming IRL (Ng & Russell, 2000), maximum entropy IRL (Ziebart et al., 2008), and deep maximum entropy IRL (Wulfmeier et al., 2015). Additionally, it features implementations of MDP domains like Gridworld (Sutton, 1998) and Objectworld (Levine et al., 2011). The repository also provides a final report detailing the implemented algorithms and offers module documentation for functions and classes.

Best used for

Ideal for data scientists, developers, and students who need to implement and experiment with inverse reinforcement learning algorithms, simulate Markov Decision Processes, and develop reward functions. Especially valuable for academic research, algorithm development, and educational purposes in AI.

Common actions

implement reinforcement learning

research AI algorithms

simulate MDPs

develop reward functions

workflowsautomated workflowdeepfakelow-code/no-codecollaborationgithub copilotface swappingopen-source"AI Agents"

Capabilities

Key features

Linear programming IRL
Maximum entropy IRL
Deep maximum entropy IRL
Gridworld MDP implementation
Objectworld MDP implementation
Value iteration functions

Target Audience

data scientistdeveloperstudent

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What inverse reinforcement learning algorithms are implemented?

The project implements linear programming IRL from Ng & Russell (2000), maximum entropy IRL from Ziebart et al. (2008), and deep maximum entropy IRL based on Wulfmeier et al. (2015). These cover various approaches to inferring reward functions from observed behavior.

What MDP domains are included for testing?

The project includes implementations of two classic Markov Decision Process (MDP) domains: Gridworld, as described by Sutton (1998), and Objectworld, from Levine et al. (2011). These provide environments for testing and demonstrating the IRL algorithms.

What are the technical requirements to run this code?

To run the Inverse-Reinforcement-Learning code, you will need NumPy, SciPy, CVXOPT, Theano, and MatPlotLib (for examples). These libraries are essential for the mathematical computations, optimization, and visualization capabilities of the project.

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