2021/12 - Review of Current State of Reinforcement Learning and Robotics

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Viviane Clay provides an overview of reinforcement learning principles and their application to robotics, addressing sensorimotor learning, Markov decision processes, and both value-based and policy-based methods.

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Thanks for the informative review!
To better understand RL’s challenges, I used ChatGPT to create a video game player analogy for each challenge. It helped me to grasp the concepts better. I share it here with you. Let me know if there are any hallucinations in the text :sweat_smile:

  1. Credit Assignment: Imagine a player who finally defeats a difficult boss after many attempts. It’s hard to pinpoint exactly which specific actions or strategies led to the victory. This is similar to the challenge of credit assignment in RL, where the agent must determine which actions led to the reward.
  2. Exploration vs. Exploitation: A player might try a new strategy (exploration) or stick to a strategy that has worked in the past (exploitation). Balancing these two is crucial for improving their gameplay.
  3. Sample Efficiency: A skilled player might learn a new game quickly, while a less experienced player might need more time and practice. Similarly, an RL agent might require many interactions with the environment to learn effectively.
  4. Model Mismatch: A player who is skilled at a specific video game might struggle with a different game, even if the two games share some similarities. This is similar to the challenge of model mismatch in RL, where an agent trained in a simulated environment might not perform well in the real world.
  5. Reward Engineering: A game designer must carefully design the game’s reward system to encourage players to explore the game world and complete challenges. Similarly, an RL agent’s behavior can be shaped by the design of the reward function.
  6. Safety and Robustness: A player must learn to avoid obstacles and enemies to survive in the game. Similarly, an RL agent must be able to operate safely in real-world environments, especially in safety-critical applications.
  7. Generalization: A skilled player can adapt to different game challenges and strategies. An RL agent should be able to generalize its learned skills to new tasks and environments.
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