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From the Grimoire: Reinforcement Learning (Part 2)
- The article delves into basic behavior cloning in reinforcement learning and its drawbacks, including quadratically compounding error and mode averaging.
- Behavior cloning involves training a neural network on expert data to create a policy that mimics expert behavior.
- The compounding errors in behavior cloning can lead to sub-optimal actions, especially in complex situations.
- Mode averaging is another issue where the policy might average behaviors and not perform optimally in different scenarios.
- The DAgger algorithm mitigates behavior cloning issues by aggregating more human data intelligently.
- DAgger extends behavior cloning by adding corrective labels at critical failure points to guide the agent back on track.
- The article provides detailed technical explanations and examples to illustrate behavior cloning problems and solutions.
- F-divergence concepts, specifically the forward KL divergence, are used to analyze the behavior cloning problem formulation.
- Efforts to rewrite the objective with different F-divergences are suggested to avoid mode averaging issues in behavior cloning.
- The discussion on KL divergence and minimizing F-divergence provides insights into the challenges faced in optimizing policies.
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