Maximum Entropy Inverse Reinforcement Learning
counts, fζ = P sj∈ζ fsj, which are the sum of the state fea- tures along the path. reward(fζ) = θ⊤fζ =X sj∈ζ θ⊤f sj The agent demonstrates single trajectories, ζ˜ i, and has an expected empirical feature count, ˜f = 1 m P i fζ˜ i, based on many (m) demonstrated trajectories.
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