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3 cards from this section. Quick, good the night before a test.
What does REINFORCE stand for in the context of reinforcement learning?
Think about how the algorithm improves agent decisions.
Describe what a policy is in reinforcement learning.
Think about how players decide their moves in a game.
What is the primary objective of the REINFORCE algorithm?
Consider what drives the algorithm's updates.
True or False: REINFORCE learns action values directly rather than optimizing the policy.
Think about the differences between the two methods.
Design an experiment to test the efficiency of the REINFORCE algorithm compared to a value-based method in a simulated environment.
Consider how you can control variables to ensure a fair comparison.
Discuss strategies to overcome the high variance challenge in REINFORCE and suggest ways to implement them in practice.
Think about how heavy fluctuations can be smoothed out.
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