scholarly journals Efficient Robot Skills Learning with Weighted Near-Optimal Experiences Policy Optimization

2021 ◽  
Vol 11 (3) ◽  
pp. 1131
Author(s):  
Liwei Hou ◽  
Hengsheng Wang ◽  
Haoran Zou ◽  
Qun Wang
Keyword(s):  
Reinforcement Learning ◽  
Learning Process ◽  
Gradient Methods ◽  
Policy Network ◽  
Practice Phase ◽  
Network Learning ◽  
Network Parameters ◽  
Model Free ◽  
Policy Gradient ◽  
Skills Learning

Autonomous learning of robotic skills seems to be more natural and more practical than engineered skills, analogous to the learning process of human individuals. Policy gradient methods are a type of reinforcement learning technique which have great potential in solving robot skills learning problems. However, policy gradient methods require too many instances of robot online interaction with the environment in order to learn a good policy, which means lower efficiency of the learning process and a higher likelihood of damage to both the robot and the environment. In this paper, we propose a two-phase (imitation phase and practice phase) framework for efficient learning of robot walking skills, in which we pay more attention to the quality of skill learning and sample efficiency at the same time. The training starts with what we call the first stage or the imitation phase of learning, updating the parameters of the policy network in a supervised learning manner. The training set used in the policy network learning is composed of the experienced trajectories output by the iterative linear Gaussian controller. This paper also refers to these trajectories as near-optimal experiences. In the second stage, or the practice phase, the experiences for policy network learning are collected directly from online interactions, and the policy network parameters are updated with model-free reinforcement learning. The experiences from both stages are stored in the weighted replay buffer, and they are arranged in order according to the experience scoring algorithm proposed in this paper. The proposed framework is tested on a biped robot walking task in a MATLAB simulation environment. The results show that the sample efficiency of the proposed framework is much higher than ordinary policy gradient algorithms. The algorithm proposed in this paper achieved the highest cumulative reward, and the robot learned better walking skills autonomously. In addition, the weighted replay buffer method can be made as a general module for other model-free reinforcement learning algorithms. Our framework provides a new way to combine model-based reinforcement learning with model-free reinforcement learning to efficiently update the policy network parameters in the process of robot skills learning.

scholarly journals Learning Unmanned Aerial Vehicle Control for Autonomous Target Following

2018 ◽  
Author(s):  
Siyi Li ◽  
Tianbo Liu ◽  
Chi Zhang ◽  
Dit-Yan Yeung ◽  
Shaojie Shen
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Unmanned Aerial Vehicle ◽  
Learning Process ◽  
Real World ◽  
Model Free ◽  
The Neural Network ◽  
Policy Gradient ◽  
Aerial Vehicle ◽  
Efficient Learning

While deep reinforcement learning (RL) methods have achieved unprecedented successes in a range of challenging problems, their applicability has been mainly limited to simulation or game domains due to the high sample complexity of the trial-and-error learning process. However, real-world robotic applications often need a data-efficient learning process with safety-critical constraints. In this paper, we consider the challenging problem of learning unmanned aerial vehicle (UAV) control for tracking a moving target. To acquire a strategy that combines perception and control, we represent the policy by a convolutional neural network. We develop a hierarchical approach that combines a model-free policy gradient method with a conventional feedback proportional-integral-derivative (PID) controller to enable stable learning without catastrophic failure. The neural network is trained by a combination of supervised learning from raw images and reinforcement learning from games of self-play. We show that the proposed approach can learn a target following policy in a simulator efficiently and the learned behavior can be successfully transferred to the DJI quadrotor platform for real-world UAV control. 

scholarly journals Hybrid-Model-Based Deep Reinforcement Learning for Heating, Ventilation, and Air-Conditioning Control

2021 ◽  
Vol 8 ◽  
Author(s):  
Huan Zhao ◽  
Junhua Zhao ◽  
Ting Shu ◽  
Zibin Pan
Keyword(s):  
Reinforcement Learning ◽  
Energy Consumption ◽  
Hybrid Model ◽  
Learning Process ◽  
Energy Cost ◽  
Air Conditioning ◽  
Electricity Price ◽  
Learning Efficiency ◽  
Model Based ◽  
Model Free

Buildings account for a large proportion of the total energy consumption in many countries and almost half of the energy consumption is caused by the Heating, Ventilation, and air-conditioning (HVAC) systems. The model predictive control of HVAC is a complex task due to the dynamic property of the system and environment, such as temperature and electricity price. Deep reinforcement learning (DRL) is a model-free method that utilizes the “trial and error” mechanism to learn the optimal policy. However, the learning efficiency and learning cost are the main obstacles of the DRL method to practice. To overcome this problem, the hybrid-model-based DRL method is proposed for the HVAC control problem. Firstly, a specific MDPs is defined by considering the energy cost, temperature violation, and action violation. Then the hybrid-model-based DRL method is proposed, which utilizes both the knowledge-driven model and the data-driven model during the whole learning process. Finally, the protection mechanism and adjusting reward methods are used to further reduce the learning cost. The proposed method is tested in a simulation environment using the Australian Energy Market Operator (AEMO) electricity price data and New South Wales temperature data. Simulation results show that 1) the DRL method can reduce the energy cost while maintaining the temperature satisfactory compared to the short term MPC method; 2) the proposed method improves the learning efficiency and reduces the learning cost during the learning process compared to the model-free method.

scholarly journals Deterministic Value-Policy Gradients

2020 ◽  
Vol 34 (04) ◽  
pp. 3316-3323
Author(s):  
Qingpeng Cai ◽  
Ling Pan ◽  
Pingzhong Tang
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Infinite Horizon ◽  
Gradient Algorithm ◽  
Continuous Control ◽  
Model Bias ◽  
Model Free ◽  
Policy Gradient ◽  
Analytical Gradients

Reinforcement learning algorithms such as the deep deterministic policy gradient algorithm (DDPG) has been widely used in continuous control tasks. However, the model-free DDPG algorithm suffers from high sample complexity. In this paper we consider the deterministic value gradients to improve the sample efficiency of deep reinforcement learning algorithms. Previous works consider deterministic value gradients with the finite horizon, but it is too myopic compared with infinite horizon. We firstly give a theoretical guarantee of the existence of the value gradients in this infinite setting. Based on this theoretical guarantee, we propose a class of the deterministic value gradient algorithm (DVG) with infinite horizon, and different rollout steps of the analytical gradients by the learned model trade off between the variance of the value gradients and the model bias. Furthermore, to better combine the model-based deterministic value gradient estimators with the model-free deterministic policy gradient estimator, we propose the deterministic value-policy gradient (DVPG) algorithm. We finally conduct extensive experiments comparing DVPG with state-of-the-art methods on several standard continuous control benchmarks. Results demonstrate that DVPG substantially outperforms other baselines.

scholarly journals KoGuN: Accelerating Deep Reinforcement Learning via Integrating Human Suboptimal Knowledge

2020 ◽  
Author(s):  
Peng Zhang ◽  
Jianye Hao ◽  
Weixun Wang ◽  
Hongyao Tang ◽  
Yi Ma ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Prior Knowledge ◽  
Learning Process ◽  
Learning Algorithm ◽  
Fuzzy Rule ◽  
Policy Network ◽  
Human Knowledge ◽  
Learning Agents ◽  
The Common ◽  
Low Performance

Reinforcement learning agents usually learn from scratch, which requires a large number of interactions with the environment. This is quite different from the learning process of human. When faced with a new task, human naturally have the common sense and use the prior knowledge to derive an initial policy and guide the learning process afterwards. Although the prior knowledge may be not fully applicable to the new task, the learning process is significantly sped up since the initial policy ensures a quick-start of learning and intermediate guidance allows to avoid unnecessary exploration. Taking this inspiration, we propose knowledge guided policy network (KoGuN), a novel framework that combines human prior suboptimal knowledge with reinforcement learning. Our framework consists of a fuzzy rule controller to represent human knowledge and a refine module to finetune suboptimal prior knowledge. The proposed framework is end-to-end and can be combined with existing policy-based reinforcement learning algorithm. We conduct experiments on several control tasks. The empirical results show that our approach, which combines suboptimal human knowledge and RL, achieves significant improvement on learning efficiency of flat RL algorithms, even with very low-performance human prior knowledge.

scholarly journals End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks

2019 ◽  
Vol 33 ◽  
pp. 3387-3395 ◽  
Author(s):  
Richard Cheng ◽  
Gábor Orosz ◽  
Richard M. Murray ◽  
Joel W. Burdick
Keyword(s):  
Reinforcement Learning ◽  
System Dynamics ◽  
Learning Process ◽  
High Performance ◽  
Continuous Control ◽  
Barrier Functions ◽  
Synthesis Algorithm ◽  
Vehicle Communication ◽  
Model Free ◽  
Vehicle To Vehicle

Reinforcement Learning (RL) algorithms have found limited success beyond simulated applications, and one main reason is the absence of safety guarantees during the learning process. Real world systems would realistically fail or break before an optimal controller can be learned. To address this issue, we propose a controller architecture that combines (1) a model-free RL-based controller with (2) model-based controllers utilizing control barrier functions (CBFs) and (3) online learning of the unknown system dynamics, in order to ensure safety during learning. Our general framework leverages the success of RL algorithms to learn high-performance controllers, while the CBF-based controllers both guarantee safety and guide the learning process by constraining the set of explorable polices. We utilize Gaussian Processes (GPs) to model the system dynamics and its uncertainties. Our novel controller synthesis algorithm, RL-CBF, guarantees safety with high probability during the learning process, regardless of the RL algorithm used, and demonstrates greater policy exploration efficiency. We test our algorithm on (1) control of an inverted pendulum and (2) autonomous carfollowing with wireless vehicle-to-vehicle communication, and show that our algorithm attains much greater sample efficiency in learning than other state-of-the-art algorithms and maintains safety during the entire learning process.

scholarly journals Soft Policy Gradient Method for Maximum Entropy Deep Reinforcement Learning

2019 ◽  
Author(s):  
Wenjie Shi ◽  
Shiji Song ◽  
Cheng Wu
Keyword(s):  
Reinforcement Learning ◽  
Maximum Entropy ◽  
Bellman Equation ◽  
Value Functions ◽  
Policy Actor ◽  
Model Free ◽  
Policy Gradient ◽  
Gradient Based ◽  
Continuous Actions ◽  
Stable Learning

Maximum entropy deep reinforcement learning (RL) methods have been demonstrated on a range of challenging continuous tasks. However, existing methods either suffer from severe instability when training on large off-policy data or cannot scale to tasks with very high state and action dimensionality such as 3D humanoid locomotion. Besides, the optimality of desired Boltzmann policy set for non-optimal soft value function is not persuasive enough. In this paper, we first derive soft policy gradient based on entropy regularized expected reward objective for RL with continuous actions. Then, we present an off-policy actor-critic, model-free maximum entropy deep RL algorithm called deep soft policy gradient (DSPG) by combining soft policy gradient with soft Bellman equation. To ensure stable learning while eliminating the need of two separate critics for soft value functions, we leverage double sampling approach to making the soft Bellman equation tractable. The experimental results demonstrate that our method outperforms in performance over off-policy prior methods.

scholarly journals End-to-End Deep Reinforcement Learning for Image-Based UAV Autonomous Control

2021 ◽  
Vol 11 (18) ◽  
pp. 8419
Author(s):  
Jiang Zhao ◽  
Jiaming Sun ◽  
Zhihao Cai ◽  
Longhong Wang ◽  
Yingxun Wang
Keyword(s):  
Reinforcement Learning ◽  
Network Architecture ◽  
Control Method ◽  
Control Policy ◽  
Input Image ◽  
Autonomous Control ◽  
Policy Network ◽  
Model Free ◽  
Control Command ◽  
End To End

To achieve the perception-based autonomous control of UAVs, schemes with onboard sensing and computing are popular in state-of-the-art work, which often consist of several separated modules with respective complicated algorithms. Most methods depend on handcrafted designs and prior models with little capacity for adaptation and generalization. Inspired by the research on deep reinforcement learning, this paper proposes a new end-to-end autonomous control method to simplify the separate modules in the traditional control pipeline into a single neural network. An image-based reinforcement learning framework is established, depending on the design of the network architecture and the reward function. Training is performed with model-free algorithms developed according to the specific mission, and the control policy network can map the input image directly to the continuous actuator control command. A simulation environment for the scenario of UAV landing was built. In addition, the results under different typical cases, including both the small and large initial lateral or heading angle offsets, show that the proposed end-to-end method is feasible for perception-based autonomous control.

On the use of the policy gradient and Hessian in inverse reinforcement learning

2020 ◽  
Vol 14 (1) ◽  
pp. 117-150
Author(s):  
Alberto Maria Metelli ◽  
Matteo Pirotta ◽  
Marcello Restelli
Keyword(s):  
Reinforcement Learning ◽  
Sequential Decision ◽  
Inverse Reinforcement Learning ◽  
Reward Function ◽  
Model Free ◽  
Learning Speed ◽  
Policy Gradient ◽  
Continuous Domains ◽  
Learning Policies ◽  
Finite Domains

Reinforcement Learning (RL) is an effective approach to solve sequential decision making problems when the environment is equipped with a reward function to evaluate the agent’s actions. However, there are several domains in which a reward function is not available and difficult to estimate. When samples of expert agents are available, Inverse Reinforcement Learning (IRL) allows recovering a reward function that explains the demonstrated behavior. Most of the classic IRL methods, in addition to expert’s demonstrations, require sampling the environment to evaluate each reward function, that, in turn, is built starting from a set of engineered features. This paper is about a novel model-free IRL approach that does not require to specify a function space where to search for the expert’s reward function. Leveraging on the fact that the policy gradient needs to be zero for an optimal policy, the algorithm generates an approximation space for the reward function, in which a reward is singled out employing a second-order criterion. After introducing our approach for finite domains, we extend it to continuous ones. The empirical results, on both finite and continuous domains, show that the reward function recovered by our algorithm allows learning policies that outperform those obtained with the true reward function, in terms of learning speed.

scholarly journals The Successful Ingredients of Policy Gradient Algorithms

2021 ◽  
Author(s):  
Sven Gronauer ◽  
Martin Gottwald ◽  
Klaus Diepold
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
Gradient Methods ◽  
Gradient Algorithms ◽  
The Sublime ◽  
Policy Gradient ◽  
Art Performance ◽  
Underlying Mechanisms

Despite the sublime success in recent years, the underlying mechanisms powering the advances of reinforcement learning are yet poorly understood. In this paper, we identify these mechanisms - which we call ingredients - in on-policy policy gradient methods and empirically determine their impact on the learning. To allow an equitable assessment, we conduct our experiments based on a unified and modular implementation. Our results underline the significance of recent algorithmic advances and demonstrate that reaching state-of-the-art performance may not need sophisticated algorithms but can also be accomplished by the combination of a few simple ingredients.

Quadrotor Motion Control Using Deep Reinforcement Learning

2021 ◽  
Author(s):  
Zifei Jiang ◽  
Alan F. Lynch
Keyword(s):  
Reinforcement Learning ◽  
Neural Nets ◽  
Neural Net ◽  
Reward Function ◽  
Model Free ◽  
Policy Gradient ◽  
Aerial Vehicle ◽  
Stochastic Controller ◽  
Policy Optimization ◽  
Gradient Approach

We present a deep neural net-based controller trained by a model-free reinforcement learning (RL) algorithm to achieve hover stabilization for a quadrotor unmanned aerial vehicle (UAV). With RL, two neural nets are trained. One neural net is used as a stochastic controller which gives the distribution of control inputs. The other maps the UAV state to a scalar which estimates the reward of the controller. A proximal policy optimization (PPO) method, which is an actor-critic policy gradient approach, is used to train the neural nets. Simulation results show that the trained controller achieves a comparable level of performance to a manually-tuned PID controller, despite not depending on any model information. The paper considers different choices of reward function and their influence on controller performance.

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