MatthewJA/Inverse-Reinforcement-Learning

Health v2 · maintenance only

Adoption not part of the score

1088 stars · 236 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A Python library implementing selected inverse reinforcement learning (IRL) algorithms, including linear programming IRL, maximum entropy IRL, and deep maximum entropy IRL. It also provides MDP domains such as Gridworld and Objectworld for experimentation.

Use cases

• recover reward functions from expert trajectories
• run maximum entropy inverse reinforcement learning
• experiment with deep maxent IRL on MDPs
• learn how IRL algorithms work with reference implementations
• evaluate recovered rewards with expected value difference

When to choose

• you need classic IRL algorithms (linear programming, maxent, deep maxent) in Python
• you want a small, citable research codebase for IRL experiments
• you need Gridworld/Objectworld testbeds for reward recovery

When to avoid

• you need modern deep RL frameworks or GPU-accelerated training at scale
• you want actively maintained code with recent dependency support (Theano is deprecated)
• you need production-ready IRL tooling rather than research code

Facets

library · maturity maintenance

reinforcement-learning machine-learning reinforcement-learning machine-learning python inverse-reinforcement-learning maxent gridworld reward-recovery mdp algorithms

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