1,080 research outputs found
Learning Adaptive Display Exposure for Real-Time Advertising
In E-commerce advertising, where product recommendations and product ads are
presented to users simultaneously, the traditional setting is to display ads at
fixed positions. However, under such a setting, the advertising system loses
the flexibility to control the number and positions of ads, resulting in
sub-optimal platform revenue and user experience. Consequently, major
e-commerce platforms (e.g., Taobao.com) have begun to consider more flexible
ways to display ads. In this paper, we investigate the problem of advertising
with adaptive exposure: can we dynamically determine the number and positions
of ads for each user visit under certain business constraints so that the
platform revenue can be increased? More specifically, we consider two types of
constraints: request-level constraint ensures user experience for each user
visit, and platform-level constraint controls the overall platform monetization
rate. We model this problem as a Constrained Markov Decision Process with
per-state constraint (psCMDP) and propose a constrained two-level reinforcement
learning approach to decompose the original problem into two relatively
independent sub-problems. To accelerate policy learning, we also devise a
constrained hindsight experience replay mechanism. Experimental evaluations on
industry-scale real-world datasets demonstrate the merits of our approach in
both obtaining higher revenue under the constraints and the effectiveness of
the constrained hindsight experience replay mechanism.Comment: accepted by CIKM201
Deep Reinforcement Learning for Robotic Tasks: Manipulation and Sensor Odometry
Research in robotics has frequently focused on artificial intelligence (AI). To increase the effectiveness of the learning process for the robot, numerous studies have been carried out. To be more effective, robots must be able to learn effectively in a shorter amount of time and with fewer resources. It has been established that reinforcement learning (RL) is efficient for aiding a robot's learning. In this dissertation, we proposed and optimized RL algorithms to ensure that our robots learn well. Research into driverless or self-driving automobiles has exploded in the last few years. A precise estimation of the vehicle's motion is crucial for higher levels of autonomous driving functionality. Recent research has been done on the development of sensors to improve the localization accuracy of these vehicles. Recent sensor odometry research suggests that Lidar Monocular Visual Odometry (LIMO) can be beneficial for determining odometry. However, the LIMO algorithm has a considerable number of errors when compared to ground truth, which motivates us to investigate ways to make it far more accurate. We intend to use a Genetic Algorithm (GA) in our dissertation to improve LIMO's performance. Robotic manipulator research has also been popular and has room for development, which piqued our interest. As a result, we researched robotic manipulators and applied GA to Deep Deterministic Policy Gradient (DDPG) and Hindsight Experience Replay (HER) (GA+DDPG+HER). Finally, we kept researching DDPG and created an algorithm named AACHER. AACHER uses HER and many independent instances of actors and critics from the DDPG to increase a robot's learning effectiveness. AACHER is used to evaluate the results in both custom and existing robot environments.In the first part of our research, we discuss the LIMO algorithm, an odometry estimation technique that employs a camera and a Lidar for visual localization by tracking features from their measurements. LIMO can estimate sensor motion via Bundle Adjustment based on reliable keyframes. LIMO employs weights of the vegetative landmarks and semantic labeling to reject outliers. LIMO, like many other odometry estimating methods, has the issue of having a lot of hyperparameters that need to be manually modified in response to dynamic changes in the environment to reduce translational errors. The GA has been proven to be useful in determining near-optimal values of learning hyperparameters. In our study, we present and propose the application of the GA to maximize the performance of LIMO's localization and motion estimates by optimizing its hyperparameters. We test our approach using the well-known KITTI dataset and demonstrate how the GA supports LIMO to lower translation errors in various datasets. Our second contribution includes the use of RL. Robots using RL can select actions based on a reward function. On the other hand, the choice of values for the learning algorithm's hyperparameters could have a big impact on the entire learning process. We used GA to find the hyperparameters for the Deep Deterministic Policy Gradient (DDPG) and Hindsight Experience Replay (HER). We proposed the algorithm GA+DDPG+HER to optimize learning hyperparameters and applied it to the robotic manipulation tasks of FetchReach, FetchSlide, FetchPush, FetchPick\&Place, and DoorOpening. With only a few modifications, our proposed GA+DDPG+HER was also used in the AuboReach environment. Compared to the original algorithm (DDPG+HER), our experiments show that our approach (GA+DDPG+HER) yields noticeably better results and is substantially faster. In the final part of our dissertation, we were motivated to use and improve DDPG. Many simulated continuous control problems have shown promising results for the DDPG, a unique Deep Reinforcement Learning (DRL) technique. DDPG has two parts: Actor learning and Critic learning. The performance of the DDPG technique is therefore relatively sensitive and unstable because actor and critic learning is a considerable contributor to the robot’s total learning. Our dissertation suggests a multi-actor-critic DDPG for reliable actor-critic learning as an improved DDPG to further enhance the performance and stability of DDPG. This multi-actor-critic DDPG is further combined with HER, called AACHER. The average value of numerous actors/critics is used to replace the single actor/critic in the traditional DDPG approach for improved resistance when one actor/critic performs poorly. Numerous independent actors and critics can also learn from the environment in general. In all the actor/critic number combinations that are evaluated, AACHER performs better than DDPG+HER
CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms
How to optimally dispatch orders to vehicles and how to tradeoff between
immediate and future returns are fundamental questions for a typical
ride-hailing platform. We model ride-hailing as a large-scale parallel ranking
problem and study the joint decision-making task of order dispatching and fleet
management in online ride-hailing platforms. This task brings unique challenges
in the following four aspects. First, to facilitate a huge number of vehicles
to act and learn efficiently and robustly, we treat each region cell as an
agent and build a multi-agent reinforcement learning framework. Second, to
coordinate the agents from different regions to achieve long-term benefits, we
leverage the geographical hierarchy of the region grids to perform hierarchical
reinforcement learning. Third, to deal with the heterogeneous and variant
action space for joint order dispatching and fleet management, we design the
action as the ranking weight vector to rank and select the specific order or
the fleet management destination in a unified formulation. Fourth, to achieve
the multi-scale ride-hailing platform, we conduct the decision-making process
in a hierarchical way where a multi-head attention mechanism is utilized to
incorporate the impacts of neighbor agents and capture the key agent in each
scale. The whole novel framework is named as CoRide. Extensive experiments
based on multiple cities real-world data as well as analytic synthetic data
demonstrate that CoRide provides superior performance in terms of platform
revenue and user experience in the task of city-wide hybrid order dispatching
and fleet management over strong baselines.Comment: CIKM 201
Automatic Parameter Optimization Using Genetic Algorithm in Deep Reinforcement Learning for Robotic Manipulation Tasks
Learning agents can make use of Reinforcement Learning (RL) to decide their
actions by using a reward function. However, the learning process is greatly
influenced by the elect of values of the parameters used in the learning
algorithm. This work proposed a Deep Deterministic Policy Gradient (DDPG) and
Hindsight Experience Replay (HER) based method, which makes use of the Genetic
Algorithm (GA) to fine-tune the parameters' values. This method (GA-DRL)
experimented on six robotic manipulation tasks: fetch-reach; fetch-slide;
fetch-push; fetch-pick and place; door-opening; and aubo-reach. Analysis of
these results demonstrated a significant increase in performance and a decrease
in learning time. Also, we compare and provide evidence that GA-DRL is better
than the existing methods
- …