We propose the integration of sentiment analysis and deep-reinforcement
learning ensemble algorithms for stock trading, and design a strategy capable
of dynamically altering its employed agent given concurrent market sentiment.
In particular, we create a simple-yet-effective method for extracting news
sentiment and combine this with general improvements upon existing works,
resulting in automated trading agents that effectively consider both
qualitative market factors and quantitative stock data. We show that our
approach results in a strategy that is profitable, robust, and risk-minimal --
outperforming the traditional ensemble strategy as well as single agent
algorithms and market metrics. Our findings determine that the conventional
practice of switching ensemble agents every fixed-number of months is
sub-optimal, and that a dynamic sentiment-based framework greatly unlocks
additional performance within these agents. Furthermore, as we have designed
our algorithm with simplicity and efficiency in mind, we hypothesize that the
transition of our method from historical evaluation towards real-time trading
with live data should be relatively simple.

我们提出了情感分析和深度强化学习集成算法用于股票交易，并设计了一种能够根据当前市场情绪动态改变其参与的交易代理的策略。我们展示了我们的方法导致了一个盈利的、稳健的和风险最小化的策略，其表现优于传统集成策略以及单一代理算法和市场指标。此外，我们设计的算法考虑了简洁性和效率，并且我们假设将我们的方法从历史评估向实时交易的转换应该相对简单。

学习市场：基于情绪的集成交易代理

Learning the Market: Sentiment-Based Ensemble Trading Agents

This paper proposes a simple strategy for sim-to-real in Deep-Reinforcement
Learning (DRL) -- called Roll-Drop -- that uses dropout during simulation to
account for observation noise during deployment without explicitly modelling
its distribution for each state. DRL is a promising approach to control robots
for highly dynamic and feedback-based manoeuvres, and accurate simulators are
crucial to providing cheap and abundant data to learn the desired behaviour.
Nevertheless, the simulated data are noiseless and generally show a
distributional shift that challenges the deployment on real machines where
sensor readings are affected by noise. The standard solution is modelling the
latter and injecting it during training; while this requires a thorough system
identification, Roll-Drop enhances the robustness to sensor noise by tuning
only a single parameter. We demonstrate an 80% success rate when up to 25%
noise is injected in the observations, with twice higher robustness than the
baselines. We deploy the controller trained in simulation on a Unitree A1
platform and assess this improved robustness on the physical system.

本文提出一种名为 Roll-Drop 的深度强化学习（DRL）的简单策略，通过在模拟期间使用 dropout 来考虑观察噪声，而不需要为每个状态显式地建模其分布。我们演示当将高达 25％的噪声注入观测后，成功率达到 80％，其鲁棒性比基线提高了一倍。在实际机器上部署受过训练的控制器并评估了所述的改进鲁棒性。

Roll-Drop：用单参数解决观测噪声的计算

Roll-Drop: accounting for observation noise with a single parameter

In this paper we design and evaluate a Deep-Reinforcement Learning agent that
optimizes routing. Our agent adapts automatically to current traffic conditions
and proposes tailored configurations that attempt to minimize the network
delay. Experiments show very promising performance. Moreover, this approach
provides important operational advantages with respect to traditional
optimization algorithms.

本文设计和评估了一个深度强化学习代理，该代理优化路由并自动适应当前交通条件，提出量身定制的配置以尝试最小化网络延迟，实验表现出极具潜力的性能与相较于传统优化算法的重要操作优势。