Absolutely! The REINFORCE algorithm is prone to biased estimates in certain situations. One scenario is when there's a significant difference in the scale of rewards across different actions or states. This can lead to a biased policy gradient estimate, favoring actions with larger rewards. To address this, reward normalization techniques or reward shaping strategies can be employed.

Indeed, biases can arise in the policy gradient estimate of the REINFORCE algorithm. One common source of bias is the presence of high-dimensional state spaces. In such cases, the variance of the policy gradient estimate can be significantly impacted, causing biased policy updates. To mitigate this, techniques like state aggregation or feature engineering can help reduce the variance and improve the quality of the estimates.

Yes, there can be scenarios where the policy gradient estimate produced by the REINFORCE algorithm is biased. One example is when the trajectory distribution induced by the policy has high variance. This can result in high variance in the estimated gradient, leading to biased updates. To mitigate this issue, techniques like baseline subtraction and variance reduction methods such as Actor-Critic algorithms can be used.