Accelerated Reinforcement Learning Research Articles

Disassembly line optimization with reinforcement learning

AbstractAs the environmental aspects become increasingly important, the disassembly problems have become the researcher’s focus. Multiple criteria do not enable finding a general optimization method for the topic, but some heuristics and classical formulations provide effective solutions. By highlighting that disassembly problems are not the straight inverses of assembly problems and the conditions are not standard, disassembly optimization solutions require human control and supervision. Considering that Reinforcement learning (RL) methods can successfully solve complex optimization problems, we developed an RL-based solution for a fully formalized disassembly problem. There were known successful implementations of RL-based optimizers. But we integrated a novel heuristic to target a dynamically pre-filtered action space for the RL agent (dlOptRL algorithm) and hence significantly raise the efficiency of the learning path. Our algorithm belongs to the Heuristically Accelerated Reinforcement Learning (HARL) method class. We demonstrated its applicability in two use cases, but our approach can also be easily adapted for other problem types. Our article gives a detailed overview of disassembly problems and their formulation, the general RL framework and especially Q-learning techniques, and a perfect example of extending RL learning with a built-in heuristic.

Accelerated Reinforcement Learning via Dynamic Mode Decomposition

This work applies the decomposition principle to discrete-time reinforcement learning to solve the optimal control problems for a network of subsystems. The control design is defined as a linear quadratic regulator graphical problem, where the performance function couples subsystems' dynamics. We first present a model-free discrete-time reinforcement learning algorithm based on online behaviors without using system dynamics. This could become a prohibitively-long learning process for larger networks. To remedy this issue, we develop an efficient model-free reinforcement learning algorithm based on dynamic mode decomposition. This decomposition method reduces the size of the measured data while the dynamic information of the original network is still retained. This algorithm is then implemented online. The proposed methodology is validated using examples of a consensus network and a power system network.

Reward shaping using directed graph convolution neural networks for reinforcement learning and games

Game theory can employ reinforcement learning algorithms to identify the optimal policy or equilibrium solution. Potential-based reward shaping (PBRS) methods are prevalently used for accelerating reinforcement learning, ensuring the optimal policy remains consistent. Existing PBRS research performs message passing based on graph convolution neural networks (GCNs) to propagate information from rewarding states. However, in an irreversible time-series reinforcement learning problem, undirected graphs will not only mislead message-passing schemes but also lose a distinctive direction structure. In this paper, a novel approach called directed graph convolution neural networks for reward shaping φDCN has been proposed to tackle this problem. The key innovation of φDCN is the extension of spectral-based undirected graph convolution to directed graphs. Messages can be efficiently propagated by leveraging a directed graph Laplacian as a substitute for the state transition matrix. As a consequence, potential-based reward shaping can then be implemented by the propagated messages. The incorporation of temporal dependencies between states makes φDCN more suitable for real-world scenarios than existing potential-based reward shaping methods based on undirected graph convolutional networks. Preliminary experiments demonstrate that the proposed φDCN exhibits a substantial improvement compared to other competing algorithms on both Atari and MuJoCo benchmarks.

A Distributed-GPU Deep Reinforcement Learning System for Solving Large Graph Optimization Problems

Graph optimization problems (such as minimum vertex cover, maximum cut, traveling salesman problems) appear in many fields including social sciences, power systems, chemistry, and bioinformatics. Recently, deep reinforcement learning (DRL) has shown success in automatically learning good heuristics to solve graph optimization problems. However, the existing RL systems either do not support graph RL environments or do not support multiple or many GPUs in a distributed setting. This has compromised the ability of reinforcement learning in solving large-scale graph optimization problems due to lack of parallelization and high scalability. To address the challenges of parallelization and scalability, we develop RL4GO , a high-performance distributed-GPU DRL framework for solving graph optimization problems. RL4GO focuses on a class of computationally demanding RL problems, where both the RL environment and policy model are highly computation intensive. Traditional reinforcement learning systems often assume either the RL environment is of low time complexity or the policy model is small. In this work, we distribute large-scale graphs across distributed GPUs and use the spatial parallelism and data parallelism to achieve scalable performance. We compare and analyze the performance of the spatial parallelism and data parallelism and show their differences. To support graph neural network (GNN) layers that take as input data samples partitioned across distributed GPUs, we design parallel mathematical kernels to perform operations on distributed 3D sparse and 3D dense tensors. To handle costly RL environments, we design a parallel graph environment to scale up all RL-environment-related operations. By combining the scalable GNN layers with the scalable RL environment, we are able to develop high-performance RL4GO training and inference algorithms in parallel. Furthermore, we propose two optimization techniques—replay buffer on-the-fly graph generation and adaptive multiple-node selection—to minimize the spatial cost and accelerate reinforcement learning. This work also conducts in-depth analyses of parallel efficiency and memory cost and shows that the designed RL4GO algorithms are scalable on numerous distributed GPUs. Evaluations on large-scale graphs show that (1) RL4GO training and inference can achieve good parallel efficiency on 192 GPUs, (2) its training time can be 18 times faster than the state-of-the-art Gorila distributed RL framework [ 34 ], and (3) its inference performance achieves a 26 times improvement over Gorila.

Accelerating Reinforcement Learning via Predictive Policy Transfer in 6G RAN Slicing

Accelerating Reinforcement Learning via Predictive Policy Transfer in 6G RAN Slicing

A heuristically accelerated reinforcement learning method for maintenance policy of an assembly line

<p style='text-indent:20px;'>This paper aims to investigate the maintenance policy for a two-machine one-buffer (2M1B) assembly line system. We assume that the observed quality states of the deteriorating machines in the system are characterized by multiple decreasing yield stages. A semi-Markov decision process (SMDP) model is used for describing the deteriorating process of the system. A heuristically accelerated multi-agent reinforcement learning (HAMRL) method is conducted to solve the problem model. The asynchronous updating rules are introduced in the HAMRL method, and the production time, preventive maintenance (PM) time and corrective repair (CR) time are random, and the deterioration mode of the device is not fixed. Meanwhile, a comparison with a simulated annealing search (SAS) based exploration algorithm and a neighborhood search (NS) based exploration algorithm in reinforcement learning (RL) is presented. The empirical results indicate that the proposed HAMRL algorithm can speed up the learning process, and has a certain advantage for the larger space and the more practical problem. And the maintenance strategy for the 2M1B assembly line system is obtained under the condition of convergent system average cost rate. This paper provides new and practical insights into the application and selection of techniques for maintenance policy of the 2M1B assembly line system.</p>

Accelerating reinforcement learning with case-based model-assisted experience augmentation for process control

In the context of intelligent manufacturing in the process industry, traditional model-based optimization control methods cannot adapt to the situation of drastic changes in working conditions or operating modes. Reinforcement learning (RL) directly achieves the control objective by interacting with the environment, and has significant advantages in the presence of uncertainty since it does not require an explicit model of the operating plant. However, most RL algorithms fail to retain transfer learning capabilities in the presence of mode variation, which becomes a practical obstacle to industrial process control applications. To address these issues, we design a framework that uses local data augmentation to improve the training efficiency and transfer learning (adaptability) performance. Therefore, this paper proposes a novel RL control algorithm, CBR-MA-DDPG, organically integrating case-based reasoning (CBR), model-assisted (MA) experience augmentation, and deep deterministic policy gradient (DDPG). When the operating mode changes, CBR-MA-DDPG can quickly adapt to the varying environment and achieve the desired control performance within several training episodes. Experimental analyses on a continuous stirred tank reactor (CSTR) and an organic Rankine cycle (ORC) demonstrate the superiority of the proposed method in terms of both adaptability and control performance/robustness. The results show that the control performance of the CBR-MA-DDPG agent outperforms the conventional PI and MPC control schemes, and that it has higher training efficiency than the state-of-the-art DDPG, TD3, and PPO algorithms in transfer learning scenarios with mode shift situations.

DiffSRL: Learning Dynamical State Representation for Deformable Object Manipulation With Differentiable Simulation

Dynamic state representation learning is essential for robot learning. Good latent space that can accurately describe dynamic transition and constraints can significantly accelerate reinforcement learning training as well as reduce motion planning complexity. However, deformable object have very complicated dynamics and is hard to be represented directly by a neural network without any prior physics information. We propose DiffSRL, an end-to-end dynamic state representation learning pipeline that uses differentiable physics engine to teach neural network how to represent high dimensional pointcloud data collected from deformable objects. Our specially designed loss function can guide neural network aware physics constraints and feasibility. We benchmark the performance of our methods as well as other state representation algorithms with multiple downstream tasks on PlasticineLab. Our model demonstrates superior performance most of the time on all tasks. We also demonstrate our model's performance in real hardware setting with two manipulation tasks on a UR-5 robot arm.

Photonic reinforcement learning based on optoelectronic reservoir computing

Reinforcement learning has been intensively investigated and developed in artificial intelligence in the absence of training data, such as autonomous driving vehicles, robot control, internet advertising, and elastic optical networks. However, the computational cost of reinforcement learning with deep neural networks is extremely high and reducing the learning cost is a challenging issue. We propose a photonic on-line implementation of reinforcement learning using optoelectronic delay-based reservoir computing, both experimentally and numerically. In the proposed scheme, we accelerate reinforcement learning at a rate of several megahertz because there is no required learning process for the internal connection weights in reservoir computing. We perform two benchmark tasks, CartPole-v0 and MountanCar-v0 tasks, to evaluate the proposed scheme. Our results represent the first hardware implementation of reinforcement learning based on photonic reservoir computing and pave the way for fast and efficient reinforcement learning as a novel photonic accelerator.

Learning Task-Distribution Reward Shaping with Meta-Learning

Reward shaping is one of the most effective methods to tackle the crucial yet challenging problem of credit assignment and accelerate Reinforcement Learning. However, designing shaping functions usually requires rich expert knowledge and hand-engineering, and the difficulties are further exacerbated given multiple tasks to solve. In this paper, we consider reward shaping on a distribution of tasks that share state spaces but not necessarily action spaces. We provide insights into optimal reward shaping, and propose a novel meta-learning framework to automatically learn such reward shaping to apply on newly sampled tasks. Theoretical analysis and extensive experiments establish us as the state-of-the-art in learning task-distribution reward shaping, outperforming previous such works (Konidaris and Barto 2006; Snel and Whiteson 2014). We further show that our method outperforms learning intrinsic rewards (Yang et al. 2019; Zheng et al. 2020), outperforms Rainbow (Hessel et al. 2018) in complex pixel-based CoinRun games, and is also better than hand-designed reward shaping on grids. While the goal of this paper is to learn reward shaping rather than to propose new general meta-learning algorithms as PEARL (Rakelly et al. 2019) or MQL (Fakoor et al. 2020), our framework based on MAML (Finn, Abbeel, and Levine 2017) also outperforms PEARL / MQL, and could combine with them for further improvement.

Efficient Reinforcement Learning for StarCraft by Abstract Forward Models and Transfer Learning

Injecting human knowledge is an effective way to accelerate reinforcement learning (RL). However, these methods are underexplored. This paper presents our discovery that an abstract forward model (thought-game (TG)) combined with transfer learning (TL) is an effective way. We take StarCraft II as our study environment. With the help of a designed TG, the agent can learn a 99% win-rate on a 64x64 map against the Level-7 built-in AI, using only 1.08 hours in a single commercial machine. We also show that the TG method is not as restrictive as it was thought to be. It can work with roughly designed TGs, and can also be useful when the environment changes. Comparing with previous model-based RL, we show TG is more effective. We also present a TG hypothesis that gives the influence of different fidelity levels of TG. For real games that have unequal state and action spaces, we proposed a novel XfrNet of which usefulness is validated while achieving a 90% win-rate against the cheating Level-10 AI. We argue that the TG method might shed light on further studies of efficient RL with human knowledge.

Collaborative Framework of Accelerating Reinforcement Learning Training with Supervised Learning Based on Edge Computing

In the reinforcement learning model training, it usually takes a lot of training data and computing time to find the law from the environmental response in order to facilitate the convergence of the model. However, edge nodes usually do not have powerful computing capabilities, which makes it impossible to apply reinforcement learning models to edge computing nodes. Therefore, the framework proposed in this study can enable the reinforcement learning model to gradually converge to the parameters of the supervised learning model within the shorter computing time, so as to solve the problem of insufficient terminal device performance in edge computing. Among the experimental results, the operating differences of hardware with different performance and the influence of the network environment and neural network architecture are analyzed based on the Mnist and Mall data sets. The result shows that it is sufficient to load the real-time required by users under the framework of collaborative training, and the time delay pressure on the model is caused by the application of different levels of complexity.

DQL energy management: An online-updated algorithm and its application in fix-line hybrid electric vehicle

With decades’ development of energy management strategy in hybrid electric vehicle, learning-based method has been deemed as a key solution for energy economy and real time. However, current energy management strategy cannot reach an optimal energy economy performance and online update in a tolerable time lag. Aiming at solving these problems, an accelerated reinforcement learning method and an online-updated strategy are proposed in present work. Firstly, prioritized replay is applied in deep Q network with normalized advantage function for a fast convergence to an optimal strategy. Prioritized replay module endows weight to trained history data which is utilized in neural network training. The neural network is updated towards optimal strategy by weight in an effective way. Secondly, the online-updated strategy for fix-line hybrid electric vehicle is designed based on the accelerated reinforcement learning method and model predictive control. The predicted future road information generated by model predictive control in each time interval is delivered to the accelerated reinforcement learning module for online energy management strategy generating. Finally, with all efforts above, the online-updated strategy is carried out and validated through hardware-in-the-loop simulation. The results show that this approach promotes the energy economic performance while updating strategy in real time.

Accelerating reinforcement learning with a Directional-Gaussian-Smoothing evolution strategy

<p style='text-indent:20px;'>The objective of reinforcement learning (RL) is to find an optimal strategy for solving a dynamical control problem. Evolution strategy (ES) has been shown great promise in many challenging reinforcement learning (RL) tasks, where the underlying dynamical system is only accessible as a black box such that adjoint methods cannot be used. However, existing ES methods have two limitations that hinder its applicability in RL. First, most existing methods rely on Monte Carlo based gradient estimators to generate search directions. Due to low accuracy of Monte Carlo estimators, the RL training suffers from slow convergence and requires more iterations to reach the optimal solution. Second, the landscape of the reward function can be deceptive and may contain many local maxima, causing ES algorithms to prematurely converge and be unable to explore other parts of the parameter space with potentially greater rewards. In this work, we employ a Directional Gaussian Smoothing Evolutionary Strategy (DGS-ES) to accelerate RL training, which is well-suited to address these two challenges with its ability to (i) provide gradient estimates with high accuracy, and (ii) find nonlocal search direction which lays stress on large-scale variation of the reward function and disregards local fluctuation. Through several benchmark RL tasks demonstrated herein, we show that the DGS-ES method is highly scalable, possesses superior wall-clock time, and achieves competitive reward scores to other popular policy gradient and ES approaches.</p>

Heuristically accelerated reinforcement learning for channel assignment in wireless sensor networks

Heuristically accelerated reinforcement learning for channel assignment in wireless sensor networks

A Memory-Greedy Policy With Guaranteed Convergence for Accelerating Reinforcement Learning

Abstract Balancing the exploration and exploitation in reinforcement learning is a commonly dilemma and time-wasting work. In this paper, a novel exploration policy used in Q-Learning, called Memory-greedy policy, is proposed to accelerate learning. By memory storage and playback, the probability of random action selecting can be effectively dealt with or reduced, which hence speeds up learning. The principle of this policy is analyzed by maze scene, and the theoretical convergence is given according to dynamic programming.

Heuristically accelerated reinforcement learning for channel assignment in wireless sensor networks

In wireless sensor networks (WSNs), multi-channel communication represents an attractive field due to its advantage in improving throughput and delivery rate. However, the major challenge that face...

Model accelerated reinforcement learning for high precision robotic assembly

Peg-in-hole assembly with narrow clearance is a typical robotic contact-rich task in industrial manufacturing. Robot learning allows robots to directly acquire the assembly skills for this task without modeling and recognizing the complex contact states. However, learning such skills is still challenging for robot because of the difficulties in collecting massive transitions data and transferring skills to new tasks, which inevitably leads to low training efficiency. This paper formulated the assembly task as a Markov decision process, and proposed a model accelerated reinforcement learning method to efficiently learn assembly policy. In this method, the assembly policy is learned with the maximum entropy reinforcement learning framework and executed with an impedance controller, which ensures exploration efficiency meanwhile allows transferring skills between tasks. To reduce sample complexity and improve training efficiency, the proposed method learns the environment dynamics with Gaussian Process while training policy, then, the learned dynamic model is utilized to improve target value estimation and generate virtual data to argument transition samples. This method can robustly learn assembly skills while minimizing real-world interaction requirements which makes it suitable for realistic assembly scenarios. To verify the proposed method, experiments on an industrial robot are conducted, and the results demonstrate that the proposed method improves the training efficiency by 31% compared with the method without model acceleration and the learned skill can be transferred to new tasks to accelerate the training for new policies.

Coaching: accelerating reinforcement learning through human-assisted approach

The learning process in reinforcement learning is time-consuming because on early episodes agent relies too much on exploration. The proposed “coaching” approach focused on helping to accelerate learning for the system with a sparse environmental reward setting. This approach works well with linear epsilon-greedy Q-learning with eligibility traces. To coach an agent, an intermediate target is given by a human coach as a sub-goal for the agent to pursue. This sub-goal provides an additional clue that guides the agent toward the actual terminal state. In the coaching phase, the agent pursues an intermediate target with an aggressive policy. The aggressive reward from this intermediate target would not be used to update the state-action value directly but the environmental reward is used. After a small number of coaching episodes, the learning would proceed normally with an $$\epsilon $$-greedy policy. In this way, the agent will end up with an optimal policy which is not under influence or supervision of a human coach. The proposed method has been tested on three experimental tasks: mountain car, ball following, and obstacle avoidance. Even with the human coach of various skill levels, the experimental results show that this method could speed up the learning process of an agent in all tasks.

Accelerated Structure-Aware Reinforcement Learning for Delay-Sensitive Energy Harvesting Wireless Sensors

We consider a time-slotted energy-harvesting wireless sensor transmitting delay-sensitive data over a fading channel. The sensor injects captured data packets into its transmission queue and relies on ambient energy harvested from the environment to transmit them. We aim to find the optimal scheduling policy that decides how many packets to transmit in each time slot to minimize the expected queuing delay. No prior knowledge of the stochastic processes that govern the channel, captured data, and harvested energy dynamics is assumed, thereby necessitating online learning to optimize the scheduling policy. We formulate this problem as a Markov decision process (MDP) with state-space spanning the sensor's buffer, battery, and channel states, and show that its optimal value function is non-decreasing and has increasing differences, in the buffer state, and that it is non-increasing and has increasing differences, in the battery state. We exploit this value function structure knowledge to formulate a novel accelerated reinforcement learning (RL) algorithm based on value function approximation that can solve the scheduling problem online with controlled approximation error, while inducing limited computational and memory complexity. We rigorously capture the trade-off between approximation accuracy and computational/memory complexity savings associated with our approach. Our simulations demonstrate that the proposed algorithm closely approximates the optimal offline solution, which requires complete knowledge of the system state dynamics. Simultaneously, our approach achieves competitive performance relative to a state-of-the-art RL algorithm, at orders of magnitude lower complexity. Moreover, considerable performance gains are demonstrated over the widely popular Q-learning RL technique.