Over the past decades, researchers have been pushing limits of deep reinforcement learning (DRL). Although DRL has attracted substantial interest from practitioners, many are blocked by having to search through a plethora available methodologies that seemingly alike, whereas others still building RL agents scratch based on classical theories. To address aforementioned gaps in adopting latest me...