jangirrishabh/toyCarIRL
Implementation of Inverse Reinforcement Learning Algorithm on a toy car in a 2D world problem, (Apprenticeship Learning via Inverse Reinforcement Learning Abbeel & Ng, 2004)
This project helps developers understand the hidden motivations or 'reward functions' behind observed behaviors, specifically for autonomous agents like a toy car in a simulated 2D world. By observing an expert agent's movements and actions (trajectories), it estimates the underlying reward structure that drives that behavior. This tool is for researchers and developers working on creating intelligent systems that learn from demonstrations without explicit programming of rewards.
178 stars. No commits in the last 6 months.
Use this if you need to understand the goals or objectives of an observed expert behavior in a simulated environment to then replicate or modify it for an autonomous agent.
Not ideal if you already know the reward function for your agent or if your goal is simple behavioral cloning without inferring underlying intentions.
Stars
178
Forks
46
Language
Python
License
MIT
Category
Last pushed
Oct 25, 2021
Commits (30d)
0
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