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Implementing the two pioneering IRL papers "Algorithms for Inverse Reinforcement Learning" - (Ng &Russell 2000) and "Maximum Entropy Inverse Reinforcement Learning" - (Ziebart et al. 2008)

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inverse_rl

Implementing the two pioneering IRL papers "Algorithms for Inverse Reinforcement Learning" - (Ng &Russell 2000) and "Maximum Entropy Inverse Reinforcement Learning" - (Ziebart et al. 2008)

Goal of the Project

Implement the paper "Algorithms for Inverse Reinforcement Learning" - (Ng & Russell 2000) and replicate results for,

  • Finite State Space: Gridworld
  • Large State Space: Mountain Car
  • Through Sampled Trajectories: Continuous Gridworld

Implement the paper "Maximum Entropy Inverse Reinforcement Learning" - (Ziebart et al. 2008) and replicate results for,

  • Simple Gridworld
  • 2-D Maze

Results

Linear Programming IRL - Discrete Gridworld

Max Entropy IRL - Discrete Gridworld

Linear Programming IRL - Mountain Car

Linear Programming IRL - Continuous Gridworld

Max Entropy IRL for 2-D Maze

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Implementing the two pioneering IRL papers "Algorithms for Inverse Reinforcement Learning" - (Ng &Russell 2000) and "Maximum Entropy Inverse Reinforcement Learning" - (Ziebart et al. 2008)

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