Can increasing the number of episodes improve the accuracy of Q-value estimates in Monte Carlo control

1. Exploration and Exploitation Tradeoff:
- More episodes allow the agent to explore the environment more, discovering new states and actions. This helps in building a more accurate estimate of the value function.
- As the agent explores more, the epsilon-greedy policy gradually shifts towards exploitation, allowing the agent to refine the policy based on the current knowledge.

2. Convergence of Q-values:
- With more episodes, the Q-value estimates become more accurate as they are updated iteratively based on the rewards received.
- The Q-values converge towards the optimal values as the number of episodes increases, as shown in the examples.

3. Transmission of Accurate Q-values:
- The terminal Q-values become more accurate as they are updated with real reward data, not just estimates.
- These accurate terminal Q-values then "trickle back" to update the Q-values earlier in the episode, improving their accuracy as well.

4. Reduced Bias and Variance:
- The search results mention that increasing the number of episodes can reduce the bias and variance of the Q-value estimates, leading to more stable and accurate policies.

5. Familiarity and Uncertainty:
- The Adaptive Estimation Q-learning (AEQ) algorithm discussed in the search results uses the concept of familiarity to give less weight to newer experiences, which can help improve the accuracy of Q-value estimates.

In summary, the search results indicate that increasing the number of episodes in Monte Carlo control can lead to more accurate Q-value estimates by allowing the agent to explore the environment more, refine the policy, and propagate accurate Q-values back through the episode. This can result in better performance and convergence to an optimal policy.