How do n-step return methods balance the trade-offs between bias and variance in reinforcement learning, and how do they address the credit assignment problem?

In the domain of reinforcement learning (RL), a important aspect involves balancing the trade-off between bias and variance to achieve optimal policy learning. N-step return methods serve as a significant approach in this context, particularly when dealing with function approximation and deep reinforcement learning. These methods are designed to harness the benefits of both Monte

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