We present a series of 25 men with prostatic abscesses studied during an 11-year interval in whom prostatic fluctuation upon digital rectal examination was the most characteristic sign. Transrectal ultrasound was the most reliable method for diagnosis and transurethral drainage under antibiotic coverage was the ideal treatment for this disease.
Dealing with sparse rewards is a long-standing challenge in reinforcement learning (RL). Hindsight Experience Replay (HER) addresses this problem by reusing failed trajectories for one goal as successful trajectories for another. This allows for both a minimum density of reward and for generalization across multiple goals. However, this strategy is known to result in a biased value function, as the update rule underestimates the likelihood of bad outcomes in a stochastic environment. We propose an asymptotically unbiased importance-sampling-based algorithm to address this problem without sacrificing performance on deterministic environments. We show its effectiveness on a range of robotic systems, including challenging high dimensional stochastic environments.
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