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    <loc>https://www.nikhilkarnwal.com/ai-lab</loc>
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    <loc>https://www.nikhilkarnwal.com/ai-lab/part-1-imitation-learning-amp-why-it-breaks</loc>
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    <lastmod>2026-04-12</lastmod>
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      <image:title>AI-Lab - Part 1: Imitation Learning &amp;amp; Why It Breaks - Make it stand out</image:title>
      <image:caption>Figure 1: The Reinforcement Learning loop. At each time step, the agent observes the current state s_t, from the environment, takes an action a_t, and in return receives a new state and a reward r_t.</image:caption>
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      <image:loc>https://images.squarespace-cdn.com/content/v1/694db376617bc461a02126ae/e8c6f25f-9c40-4477-a125-9701d318950c/ant_v5_expert.gif</image:loc>
      <image:title>AI-Lab - Part 1: Imitation Learning &amp;amp; Why It Breaks - Make it stand out</image:title>
      <image:caption>Figure 2: The expert SAC policy walking the Ant-v5 robot. This is our gold standard — the behavior we want to imitate.</image:caption>
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      <image:loc>https://images.squarespace-cdn.com/content/v1/694db376617bc461a02126ae/d583fb8c-b89d-4c52-8e68-006c1455bc11/ant_v5_bc_loss.png</image:loc>
      <image:title>AI-Lab - Part 1: Imitation Learning &amp;amp; Why It Breaks - Make it stand out</image:title>
      <image:caption>BC training loss (MSE) dropping over 100 epochs. Lower means the policy's predictions are closer to the expert's actions.</image:caption>
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    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/694db376617bc461a02126ae/9eb653c5-c8df-4c54-a39b-c7ea4fb90134/ant_v5_bc_d_shift.png</image:loc>
      <image:title>AI-Lab - Part 1: Imitation Learning &amp;amp; Why It Breaks - Make it stand out</image:title>
      <image:caption>Expert is maintaining stable height around 0.6(lower compare to BC) and able to walk faster compare to BC.</image:caption>
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      <image:loc>https://images.squarespace-cdn.com/content/v1/694db376617bc461a02126ae/be6bedbc-e832-4c71-b45d-2be2cff236f1/ant_v5_bc_dagger_loss.png</image:loc>
      <image:title>AI-Lab - Part 1: Imitation Learning &amp;amp; Why It Breaks - Make it stand out</image:title>
      <image:caption>Full comparison: DAgger progressively closes the gap with the expert as it aggregates more data.</image:caption>
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      <image:loc>https://images.squarespace-cdn.com/content/v1/694db376617bc461a02126ae/7afb55f0-c327-40f3-bf76-e54fe3bcfc8d/ant_v5_comp_render.gif</image:loc>
      <image:title>AI-Lab - Part 1: Imitation Learning &amp;amp; Why It Breaks - Make it stand out</image:title>
      <image:caption>Whatever it is, the way you tell your story online can make all the difference.</image:caption>
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    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/694db376617bc461a02126ae/d7ee288f-e078-4598-bb6b-4f8befff6b8f/ant_v5_bc_dagger_d_shift.png</image:loc>
      <image:title>AI-Lab - Part 1: Imitation Learning &amp;amp; Why It Breaks - Make it stand out</image:title>
      <image:caption>DAgger closes the distribution gap. Its state distribution (purple) is much closer to the expert's (blue) than BC's was (red).</image:caption>
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    <lastmod>2025-12-29</lastmod>
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