Measuring and characterizing generalization in deep reinforcement learning

Learning ability is the basic characteristic of human intelligence. The July 1, 2005 issue of Science published a list of 125 important questions in science. Among them, the question 94 “What are the limits of learning by machines?”. The annotation “Computers can already beat the world’s best chess players, and they have a wealth of information on the Web to draw on. But abstract reasoning is still beyond any machine”. In recent artificial intelligence has made great progresses. In 1997, the rise of the man-machine war, IBM Supercomputer Deep Blue defeated the chess master Garry Kasparov. On February 14, 2011, IBM’s Watson supercomputer won a practice round against Jeopardy champions Ken Jennings and Brad Rutter. In March 2016, Google DeepMind’s AlphaGo sealed a 4-1 victory over a South Korean Go grandmaster Lee Se-dol. This paper focuses on the machine learning methods of AlphaGo, including reinforcement learning, deep learning, deep reinforcement learning, analysis of the existing problems and the latest research progress. Deep reinforcement learning is the combination of deep learning and reinforcement learning, which can realize the learning algorithm from the perception to action. Simply said, this is the same as human behavior, input sensing information such as vision, and then, direct output action through the deep neural network. Deep reinforcement learning has the potential to learn a variety of skills for the robot to achieve full autonomy. Even though reinforcement learning is practiced successfully, but feature states need to manually set, for complex scene is a difficult thing, especially easy to cause the dimension disaster, and expression is not good. In 2010, Sascha Lange and Martin Riedmiller proposed deep auto-encoder neural networks in reinforcement learning to extract feature, which is used to control the visual correlation. In 2013, DeepMind proposed deep Q-network (DQN) in NIPS 2013, using convolution neural network to extract features, and then applied in reinforcement learning. They continue to improve and published an improved version of DQN on Nature in 2015, which has aroused widespread concern. In order to break through the limits of learning by machines, cognitive machine learning is proposed, which is the combination of machine learning and brain cognition, so that the machine intelligence is constantly evolving, and gradually reaches the human level of artificial intelligence. A cognitive model entitled Consciousness And Memory (CAM) is proposed by author, which consists of memory, consciousness, high-level cognitive functions, perception and motor. High-level cognitive functions of the brain include learning, language, thinking, decision making, emotion, and so on. Learning is a course to accept the stimulus through the nervous system and obtain new behavior, habits and accumulation experience. According to the current research progress of brain science and cognitive science, cognitive machine learning may be interested in learning emergence, procedural memory knowledge learning, learning evolution and so on. For intelligence, so-called evolution is refers to the learning of learning and the structure also follows the change. It is important to record the learning result by structure changing and improve the learning method.

Reinforcement learning is a mathematical framework for agents to interact intelligently with their environment. Unlike supervised learning, where a system learns with the help of labeled data, reinforcement learning agents learn how to act by trial and error only receiving a reward signal from their environments. A field where reinforcement learning has been prominently successful is robotics [3]. However, real-world control problems are also particularly challenging because of the noise and high- dimensionality of input data (e.g., visual input). In recent years, in the field of supervised learning, deep neural networks have been successfully used to extract meaning from this kind of data. Building on these advances, deep reinforcement learning was used to solve complex problems like Atari games and Go. Mnih et al. [1] built a system with fixed hyper parameters able to learn to play 49 different Atari games only from raw pixel inputs. However, in order to apply the same methods to real-world control problems, deep reinforcement learning has to be able to deal with continuous action spaces. Discretizing continuous action spaces would scale poorly, since the number of discrete actions grows exponentially with the dimensionality of the action. Furthermore, having a parametrized policy can be advantageous because it can generalize in the action space. Therefore with this thesis we study state-of-the-art deep reinforcement learning algorithm, Deep Deterministic Policy Gradients. We provide a theoretical comparison to other popular methods, an evaluation of its performance, identify its limitations and investigate future directions of research. The remainder of the thesis is organized as follows. We start by introducing the field of interest, machine learning, focusing our attention of deep learning and reinforcement learning. We continue by describing in details the two main algorithms, core of this study, namely Deep Q-Network (DQN) and Deep Deterministic Policy Gradients (DDPG). We then provide implementatory details of DDPG and our test environment, followed by a description of benchmark test cases. Finally, we discuss the results of our evaluation, identifying limitations of the current approach and proposing future avenues of research.

In this work, we introduce Attention-based Partially Decoupled Actor-Critic (APDAC), an actor-critic architecture for generalization in reinforcement learning, which partially separates the policy and the value functions. To learn directly from images, traditional actor-critic architectures use a shared network to represent the policy and value functions. While a shared representation allows parameter and feature sharing, it can also lead to overfitting that catastrophically damages generalization performance. On the other hand, two separate networks for policy and value can help to avoid overfitting and reduce the generalization gap, but at the cost of added complexity both in terms of architecture design and computation time. APDAC is a hybrid architecture that builds upon the combined strengths of both architectures by sharing initial layer blocks of the network and separating the later ones for policy and value. APDAC incorporates an attention mechanism to enable robust representation learning. We present meaningful visualization of the policy and value that explains the perception of the trained agent. Our empirical analysis, including an ablation study, shows that APDAC significantly outperforms the standard PPO baseline on the challenging RL generalization benchmark Procgen and achieves performance that is competitive with the recent state-of-the-art method (IDAAC) while using fewer convolutional layers and requiring less computational time. Our code is available at https://github.com/nasiknafi/apdac.

To decrease the impact of Partially Observable MDP on deep reinforcement learning performance of intersection signal control, A deep reinforcement learning is proposed in this paper with utilizing the real-time GPS data as well as learning the control of the traffic lights in single intersection. We integrate deep reinforcement learning network (DRQN) with recurrent neural network (RNN) and apply deep network, experience pool and greedy strategy in deep reinforcement learning strategy. It solves the problem of overestimation of target Q value and insufficient long-term experience learning in the standard reinforcement learning of traffic signal control. The comparison of performance was made between the proposed method and standard Deep Q-Network (DQN) on the partial observation of traffic situations. The experimental results show that both DQN and DRQN methods can adjust their traffic signal timing control strategies according to the specific traffic conditions as well as calculating a lower average delay time of vehicle than that of fixed-time control. Besides, the simulation effect of DRQN learning method is better than that of DQN learning method in different probe vehicle proportion environment.

Deep reinforcement learning (DRL) algorithms have been widely applied in user cold-start recommender systems because they can gradually capture users' dynamic interest preferences. Deep Q-Networks (DQN) have become the most popular reinforcement learning (RL) method due to their simple update strategy and excellent performance. In many user cold-start scenarios, the action space is gradually reduced to avoid recommending duplicate items to users. However, current DQN-based RL recommender systems output the entire action space fixedly, inevitably leading to discrepancies with the gradually shrinking action space. This paper demonstrates that such discrepancies cause a decrement error in the action space corresponding to the temporal difference (TD) in the original RL, rendering standard DQN reinforcement learning methods inaccurate in Q-value estimation. Moreover, in long-term recommendation scenarios, the differences in the lengths of interactions recommended to different users are significant, making it difficult to ignore such errors, thereby challenging the applicability of these methods in scenarios where the action space gradually reduces. To address this issue, this paper introduces a new algorithm called Q-AD (Q-learning Action Decrease), which is based on DQN and aims to mitigate the reduction error in the action space by buffering the Q-value estimation error at each update. Q-AD augments the standard DQN with an error reduction term for TD updates. Through experiments, it was observed that the Q-AD algorithm significantly reduces value estimation errors and achieves better accuracy and efficiency compared to previous methods across different datasets.

To solve problems involving sequential decision-making, Deep Reinforcement Learning (DRL) combines the advantages of Reinforcement Learning (RL) and Deep Learning (DL).These fusions are skilled at training agents for video games because they use neural networks to approximate nonlinear functions.The Deep Q Network (DQN), a key algorithm in this field, tends to overestimate Q-values despite its effectiveness.The Double Deep Q Network (Double DQN) was presented to address this.The Kera's framework in PyCharm is used in this study to examine the practical application and comparative analysis of DQN and Double DQN in the Flappy Bird game.To speed up its training, improvements were also made to the DQN model.The enhanced DQN performed better than the traditional DQN but less well than the Double DQN, according to the results.This study delves into the practical application and comparative analysis of DQN and Double DQN in the Flappy Bird game, utilizing the Kera's framework in PyCharm.Additionally, enhancements were made to the DQN model to expedite its training.Results affirmed that the enhanced DQN outperformed the conventional DQN but lagged the Double DQN.The training loss trajectory further substantiated Double DQN's superiority in mitigating overestimation issues.

Deep reinforcement learning based energy management for a hybrid electric vehicle

Deep reinforcement learning (DRL) is one of the emerging areas of machine learning which focuses on maximized rewards. DRL is a type of machine learning that combines reinforcement learning and deep learning. It uses a series of algorithms to enable an agent to learn how to make decisions in a complex environment. DRL is a subset of artificial intelligence that focuses on making decisions based on the environment and the rewards associated with each action.The goal of DRL is to maximize the long-term reward of an agent. In order to do this, the agent must use a combination of deep learning, reinforcement learning and other AI techniques to learn which actions will lead to the highest reward. DRL is used to solve a variety of problems, from playing video games to controlling robots. It is also used in autonomous driving and robotics, as well as for financial trading. DRL is a powerful tool for solving complex problems and has been used in a variety of research projects. DRL has the potential to revolutionize the way we interact with machines and the environment.

With the rapid development of AI technology, personalized learning path planning is becoming an important research field. Dual system teaching models have great influence on the learning effect of students. Therefore, how to optimize the learning process of students through the personalized learning path is a great challenge. A Deep Reinforcement Learning (DRL) algorithm for personalized learning path planning is proposed. Based on deep Q-networks and reinforcement learning feedback mechanisms, the algorithm achieves adaptive optimization. The experimental results show that the algorithm based on the DRL is superior to the traditional method in learning performance, learning efficiency and satisfaction. Compared with traditional path planning algorithms based on rules and collaborative filtering, the proposed algorithm improves learning performance by 20%. Moreover, it has obvious advantages in learning motivation and efficiency. This paper provides a new way of thinking to intelligent education system, especially dual-system teaching model, and it is of great practical significance.

Reinforcement learning (RL), a promising framework for data-driven decision making in an uncertain environment, has successfully been applied in many real-world operation and control problems. However, the application of RL in a large-scale decentralized multi-agent environment remains a challenging problem due to the partial observability and limited communications between agents. In this paper, we develop a model-based kernel RL approach and a model-free deep RL approach for learning a decentralized, shared policy among homogeneous agents. By leveraging the strengths of both these methods, we further propose a novel deep ensemble multi-agent reinforcement learning (MARL) method that efficiently learns to arbitrate between the decisions of the local kernel-based RL model and the wider-reaching deep RL model. We validate the proposed deep ensemble method on a highly challenging real-world air traffic control problem, where the goal is to provide effective guidance to aircraft to avoid air traffic congestion, conflicting situations, and to improve arrival timeliness, by dynamically recommending adjustments of aircraft speeds in real-time. Extensive empirical results from an open-source air traffic management simulation model, developed by Eurocontrol and built on a real-world data set including thousands of aircrafts, demonstrate that our proposed deep ensemble MARL method significantly outperforms three state-of-the-art benchmark approaches.

At present, the global COVID-19 is still severe. More and more countries have experienced second or even third outbreaks. The epidemic is far from over until the vaccine is successfully developed and put on the market on a large scale. Inappropriate epidemic control strategies may bring catastrophic consequences. It is essential to maximize the epidemic restraining and to mitigate economic damage. However, the study on the optimal control strategy concerning both sides is rare, and no optimal model has been built. In this paper, the Susceptible-Infectious-Hospitalized-Recovered (SIHR) compartment model is expanded to simulate the epidemic’s spread concerning isolation rate. An economic model affected by epidemic isolation measures is established. The effective reproduction number and the eigenvalues at the equilibrium point are introduced as the indicators of controllability and stability of the model and verified the effectiveness of the SIHR model. Based on the Deep Q Network (DQN), one of the deep reinforcement learning (RL) methods, the blocking policy is studied to maximize the economic output under the premise of controlling the number of infections in different stages. The epidemic control strategies given by deep RL under different learning strategies are compared for different reward coefficients. The study demonstrates that optimal policies may differ in various countries depending on disease spread and anti-economic risk ability. The results show that the more economical strategy, the less economic loss in the short term, which can save economically fragile countries from economic crises. In the second or third outbreak stage, the earlier the government adopts the control strategy, the smaller the economic loss. We recommend the method of deep RL to specify a policy which can control the epidemic while making quarantine economically viable.

This paper investigates the vision-based autonomous driving with deep learning and reinforcement learning methods. Different from the end-to-end learning method, our method breaks the vision-based lateral control system down into a perception module and a control module. The perception module which is based on a multi-task learning neural network first takes a driver-view image as its input and predicts the track features. The control module which is based on reinforcement learning then makes a control decision based on these features. In order to improve the data efficiency, we propose visual TORCS (VTORCS), a deep reinforcement learning environment which is based on the open racing car simulator (TORCS). By means of the provided functions, one can train an agent with the input of an image or various physical sensor measurement, or evaluate the perception algorithm on this simulator. The trained reinforcement learning controller outperforms the linear quadratic regulator (LQR) controller and model predictive control (MPC) controller on different tracks. The experiments demonstrate that the perception module shows promising performance and the controller is capable of controlling the vehicle drive well along the track center with visual input.

Humans have to respond to everyday tasks with goal-directed actions in complex and time-continuous environments. However, modeling human behavior in such environments has been challenging. Deep Q-networks (DQNs), an application of deep learning used in reinforcement learning (RL), enable the investigation of how humans transform high-dimensional, time-continuous visual stimuli into appropriate motor responses. While recent advances in DQNs have led to significant performance improvements, it has remained unclear whether these advancements translate into improved modeling of human behavior. Here, we recorded motor responses in human participants (N = 23) while playing three distinct arcade games. We used stimulus features generated by a DQN as predictors for human data by fitting the DQN’s response probabilities to human motor responses using a linear model. We hypothesized that advancements in RL models would lead to better prediction of human motor responses. Therefore, we used features from two recently developed DQN models (Ape-X and SEED) and a third baseline DQN to compare prediction accuracy. Compared to the baseline DQN, Ape-X and SEED involved additional structures, such as dueling and double Q-learning, and a long short-term memory, which considerably improved their performances when playing arcade games. Since the experimental tasks were time-continuous, we also analyzed the effect of temporal resolution on prediction accuracy by smoothing the model and human data to varying degrees. We found that all three models predict human behavior significantly above chance level. SEED, the most complex model, outperformed the others in prediction accuracy of human behavior across all three games. These results suggest that advances in deep RL can improve our capability to model human behavior in complex, time-continuous experimental tasks at a fine-grained temporal scale, thereby opening an interesting avenue for future research that complements the conventional experimental approach, characterized by its trial structure and use of low-dimensional stimuli.

With the improvement in the intelligence level of UAVs and the development of cluster control technology, the intelligent decision-making method of UAV cluster confrontation will become the key technology of UAV combat in the future. The UAV cluster confrontation learning environment is of complex characteristics of high dimension, non-linearity, incomplete information, continuous action space, and so on. Recently, artificial intelligence technology represented by deep learning and reinforcement learning has made a great breakthrough. Deep reinforcement learning has shown a great ability to solve intelligent decision-making problems in complex environments. In this paper, enlightened by the multi-agent centralized training distributed execution framework and the idea of maximum policy entropy, we propose a deep reinforcement learning method of the multi-agent soft actor-critic (MASAC) with incomplete information. A game model for UAV cluster confrontation based on multi-agent deep reinforcement learning is established, and a continuous space multiple UAV combat environment is constructed. Simulation experiments are performed on the asymmetric confrontation of UAV clusters with red and blue teams. The experimental results show that MASAC outperforms the existing popular multi-agent deep reinforcement learning methods, making the game players converge to a higher return equilibrium point of the game. Moreover, the convergence of MASAC is investigated and analyzed extensively, and the results show that MASAC is of good convergence and stability, ensuring the practicability of MASAC in intelligent decision-making regarding UAV cluster confrontation.

Deep Reinforcement Learning (RL) suffer from catastrophic forgetting due to being agnostic to the timescale of changes in the distribution of experiences. Although, RL algorithms are guaranteed to converge to optimal policies in Markov decision processes, this only holds in the presence of static environments. However, this assumption is very restrictive. In many real world problems like ridesharing, traffic control, etc., we are dealing with highly dynamic environments, where RL methods yield only sub-optimal decisions. In this paper, we introduce an adaptive model-free deep reinforcement approach that can recognize diurnal patterns in the ridesharing environment. To achieve this, we (1) adopt a change point detection algorithm to detect the changes in the distribution of experiences, then (2) we develop a Deep Q Network (DQN) agent that is capable of recognizing diurnal patterns and making informed dispatching decisions according to the changes in the underlying environment. Based on the demand, our DQN approach re-balances idle vehicles by dispatching them to the areas of anticipated high demand using Deep Reinforcement Learning. This approach can be adopted in various domains through tuning the RL agent's objective function, where it will still capture the changes in the corresponding underlying environment. Our framework is validated using the New York City Taxi public dataset. Experimental results show the effectiveness of our approach in real-time and large scale settings.