When DQN is announced by deepmind in 2013, the whole world is surprised by the simplicity and promising result, but due to the low efficiency and stability of this method, it is hard to solve many problems. After all these years, people purposed more and more complicated ideas for improving, many of them use distributed Deep-RL which needs tons of cores to run the simulators. However, the basic ideas behind all this technique are sometimes just a modified DQN. So we asked a simple question, is there a more elegant way to improve the DQN model? Instead of adding more and more small fixes on it, we redesign the problem setting under a popular entropy regularization framework which leads to better performance and theoretical guarantee. Finally, we purposed SQN, a new off-policy algorithm with better performance and stability.

We propose a reinforcement learning framework for discrete environments in which an agent makes both strategic and tactical decisions. The former manifests itself through the use of value function, while the latter is powered by a tree search planner. These tools complement each other. The planning module performs a local \textit{what-if} analysis, which allows to avoid tactical pitfalls and boost backups of the value function. The value function, being global in nature, compensates for inherent locality of the planner. In order to further solidify this synergy, we introduce an exploration mechanism with two distinctive components: uncertainty modelling and risk measurement. To model the uncertainty we use value function ensembles, and to reflect risk we use propose several functionals that summarize the implied by the ensemble. We show that our method performs well on hard exploration environments: Deep-sea, toy Montezuma's Revenge, and Sokoban. In all the cases, we obtain speed-up in learning and boost in performance.

Entropy regularization is an imported idea in reinforcement learning, with great success in recent algorithms like Soft Actor Critic and Soft Q Network. In this work we extend this idea into the on-policy realm. With the soft gradient policy theorem, we construct the maximum entropy reinforcement learning framework for on-policy RL. For policy gradient based on-policy algorithms, policy network is often represented as Gaussian distribution with the action variance restricted to be global for all the states observed from the environment. We propose an idea called action variance scale for policy network and find it can work collaboratively with the idea of entropy regularization. In this paper, we choose the state-of-the-art on-policy algorithm, Proximal Policy Optimization, as our basal algorithm and present Soft Proximal Policy Optimization (SPPO). PPO is a popular on-policy RL algorithm with great stability and parallelism. But like many on-policy algorithm, PPO can also suffer from low sample efficiency and local optimum problem. In the entropy-regularized framework, SPPO can guide the agent to succeed at the task while maintaining exploration by acting as randomly as possible. Our method outperforms prior works on a range of continuous control benchmark tasks, Furthermore, our method can be easily extended to large scale experiment and achieve stable learning at high throughput.

This paper presents a novel motion and trajectory planning algorithm for nonholonomic mobile robots that uses recent advances in deep reinforcement learning. Starting from a random initial state, i.e., position, velocity and orientation, the robot reaches an arbitrary target state while taking both kinematic and dynamic constraints into account. Our deep reinforcement learning agent not only processes a continuous state space it also executes continuous actions, i.e., the acceleration of wheels and the adaptation of the steering angle. We evaluate our motion and trajectory planning on a mobile robot with a differential drive in a simulation environment.

If one policy has greater or equal value than the other, in all states, we might say the policy is better. The policy gradient paper guarantees that locally optimal policies can be found with function approximation. This functional returns either the long-term avg rewards or discounted cumulative rewards from a designated start state. In practice, one would obtain an unbiased estimator for the gradient of this functional w.r.t.

The simultaneous control of multiple coordinated robotic agents represents an elaborate problem. If solved, however, the interaction between the agents can lead to solutions to sophisticated problems. The concept of swarming, inspired by nature, can be described as the emergence of complex system-level behaviors from the interactions of relatively elementary agents. Due to the effectiveness of solutions found in nature, bio-inspired swarming-based control techniques are receiving a lot of attention in robotics. One method, known as swarm shepherding, is founded on the sheep herding behavior exhibited by sheepdogs, where a swarm of relatively simple agents are governed by a shepherd (or shepherds) which is responsible for high-level guidance and planning. Many studies have been conducted on shepherding as a control technique, ranging from the replication of sheep herding via simulation, to the control of uninhabited vehicles and robots for a variety of applications. We present a comprehensive review of the literature on swarm shepherding to reveal the advantages and potential of the approach to be applied to a plethora of robotic systems in the future.

We propose an explainable reinforcement learning (XRL) framework that analyzes an agent's history of interaction with the environment to extract interestingness elements that help explain its behavior. The framework relies on data readily available from standard RL algorithms, augmented with data that can easily be collected by the agent while learning. We describe how to create visual explanations of an agent's behavior in the form of short video-clips highlighting key interaction moments, based on the proposed elements. We also report on a user study where we evaluated the ability of humans in correctly perceiving the aptitude of agents with different characteristics, including their capabilities and limitations, given explanations automatically generated by our framework. The results show that the diversity of aspects captured by the different interestingness elements is crucial to help humans correctly identify the agents' aptitude in the task, and determine when they might need adjustments to improve their performance.

Global Autoregressive Models (GAMs) are a recent proposal [Parshakova et al., CoNLL 2019] for exploiting global properties of sequences for data-efficient learning of seq2seq models. In the first phase of training, an Energy-Based model (EBM) over sequences is derived. This EBM has high representational power, but is unnormalized and cannot be directly exploited for sampling. To address this issue [Parshakova et al., CoNLL 2019] proposes a distillation technique, which can only be applied under limited conditions. By relating this problem to Policy Gradient techniques in RL, but in a \emph{distributional} rather than \emph{optimization} perspective, we propose a general approach applicable to any sequential EBM. Its effectiveness is illustrated on GAM-based experiments.

A novel approach of applying deep reinforcement learning to an RF pulse design is introduced. This method, which is referred to as $DeepRF_{SLR}$, is designed to minimize the peak amplitude or, equivalently, minimize the pulse duration of a multiband refocusing pulse generated by the Shinar Le-Roux (SLR) algorithm. In the method, the root pattern of SLR polynomial, which determines the RF pulse shape, is optimized by iterative applications of deep reinforcement learning and greedy tree search. When tested for the designs of the multiband factors of three and seven RFs, $DeepRF_{SLR}$ demonstrated improved performance compared to conventional methods, generating shorter duration RF pulses in shorter computational time. In the experiments, the RF pulse from $DeepRF_{SLR}$ produced a slice profile similar to the minimum-phase SLR RF pulse and the profiles matched to that of the computer simulation. Our approach suggests a new way of designing an RF by applying a machine learning algorithm, demonstrating a machine-designed MRI sequence.

We present hierarchical Deep Q-Network with Forgetting (HDQF) that took first place in MineRL competition. HDQF works on imperfect demonstrations utilize hierarchical structure of expert trajectories extracting effective sequence of meta-actions and subgoals. We introduce structured task dependent replay buffer and forgetting technique that allow the HDQF agent to gradually erase poor-quality expert data from the buffer. In this paper we present the details of the HDQF algorithm and give the experimental results in Minecraft domain.