Intelligent Trainer for Dyna-Style Model-Based Deep Reinforcement Learning.

Model-based reinforcement learning (MBRL) has been proposed as a promising alternative solution to tackle the high sampling cost challenge in the canonical RL, by leveraging a system dynamics model to generate synthetic data for policy training purpose. The MBRL framework, nevertheless, is inherently limited by the convoluted process of jointly optimizing control policy, learning system dynamics, and sampling data from two sources controlled by complicated hyperparameters. As such, the training process involves overwhelmingly manual tuning and is prohibitively costly. In this research, we propose a "reinforcement on reinforcement" (RoR) architecture to decompose the convoluted tasks into two decoupled layers of RL. The inner layer is the canonical MBRL training process which is formulated as a Markov decision process, called training process environment (TPE). The outer layer serves as an RL agent, called intelligent trainer, to learn an optimal hyperparameter configuration for the inner TPE. This decomposition approach provides much-needed flexibility to implement different trainer designs, referred to "train the trainer." In our research, we propose and optimize two alternative trainer designs: 1) an unihead trainer and 2) a multihead trainer. Our proposed RoR framework is evaluated for five tasks in the OpenAI gym. Compared with three other baseline methods, our proposed intelligent trainer methods have a competitive performance in autotuning capability, with up to 56% expected sampling cost saving without knowing the best parameter configurations in advance. The proposed trainer framework can be easily extended to tasks that require costly hyperparameter tuning.

Distribution and sources of polycyclic aromatic hydrocarbons in surface sediments from the Bering Sea and western Arctic Ocean.

To analyze the distribution and sources of polycyclic aromatic hydrocarbons (PAHs) and evaluate their potential ecological risks, the concentrations of 16 PAHs were measured in 43 surface sediment samples from the Bering Sea and western Arctic Ocean. Total PAH (tPAH) concentrations ranged from 36.95 to 150.21 ng/g (dry weight). In descending order, the surface sediment tPAH concentrations were as follows: Canada Basin>northern Chukchi Sea>Chukchi Basin>southern Chukchi Sea>Aleutian Basin>Makarov Basin>Bering Sea shelf. The Bering Sea and western Arctic Ocean mainly received PAHs of pyrogenic origin due to pollution caused by the incomplete combustion of fossil fuels. The concentrations of PAHs in the sediments of the study areas did not exceed effects range low (ERL) values.