Cliff Notes
In the classic gridworld 'Cliff Walking' example, an agent training with SARSA tends to converge on a safer route that stays away from a cliff edge, while an agent training with Q-learning converges on the riskier, truly optimal path right along the edge (even though both use epsilon-greedy behavior during training). Why?
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