#### Faster Deep Q-learning using Neural Episodic Control

DNN DQN Reinforcement Learning

The Research on deep reinforcement learning to estimate Q-value by deep learning has been active in recent years. In deep reinforcement learning, it is important to efficiently learn the experiences that a agent has collected by exploring the environment. In this research, we propose NEC2DQN that i…

#### Deep Reinforcement Fuzzing

DQN Reinforcement Learning security

Fuzzing is the process of finding security vulnerabilities in input-processing code by repeatedly testing the code with modified inputs. In this paper, we formalize fuzzing as a reinforcement learning problem using the concept of Markov decision processes. This in turn allows us to apply state-of-t…

#### DeepTraffic: Driving Fast through Dense Traffic with Deep Reinforcement Learning

car DQN Reinforcement Learning

We present a micro-traffic simulation (named “DeepTraffic”) where the perception, control, and planning systems for one of the cars are all handled by a single neural network as part of a model-free, off-policy reinforcement learning process. The primary goal of DeepTraffic is to make t…

#### Whatever Does Not Kill Deep Reinforcement Learning, Makes It Stronger

Recent developments have established the vulnerability of deep Reinforcement Learning (RL) to policy manipulation attacks via adversarial perturbations. In this paper, we investigate the robustness and resilience of deep RL to training-time and test-time attacks. Through experimental results, we de…

#### A Deep Policy Inference Q-Network for Multi-Agent Systems

DQN game Reinforcement Learning RNN

We present DPIQN, a deep policy inference Q-network that targets multi-agent systems composed of controllable agents, collaborators, and opponents that interact with each other. We focus on one challenging issue in such systems—modeling agents with varying strategies—and propose to empl…

#### Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning

DNN DQN game Genetic Algorithm gradient Reinforcement Learning

Deep artificial neural networks (DNNs) are typically trained via gradient-based learning algorithms, namely backpropagation. Evolution strategies (ES) can rival backprop-based algorithms such as Q-learning and policy gradients on challenging deep reinforcement learning (RL) problems. However, ES ca…

#### Combating Reinforcement Learning’s Sisyphean Curse with Intrinsic Fear

DQN game Reinforcement Learning

To use deep reinforcement learning in the wild, we might hope for an agent that can avoid catastrophic mistakes. Unfortunately, even in simple environments, the popular deep Q-network (DQN) algorithm is doomed by a Sisyphean curse. Owing to the use of function approximation, these agents may eventu…

#### Combating Reinforcement Learning’s Sisyphean Curse with Intrinsic Fear

DQN game Reinforcement Learning

To use deep reinforcement learning in the wild, we might hope for an agent that can avoid catastrophic mistakes. Unfortunately, even in simple environments, the popular deep Q-network (DQN) algorithm is doomed by a Sisyphean curse. Owing to the use of function approximation, these agents may eventu…

#### Combating Reinforcement Learning’s Sisyphean Curse with Intrinsic Fear

DQN game Reinforcement Learning

To use deep reinforcement learning in the wild, we might hope for an agent that can avoid catastrophic mistakes. Unfortunately, even in simple environments, the popular deep Q-network (DQN) algorithm is doomed by a Sisyphean curse. Owing to the use of function approximation, these agents may eventu…

#### Combating Reinforcement Learning’s Sisyphean Curse with Intrinsic Fear

DQN game Reinforcement Learning

#### Combating Reinforcement Learning’s Sisyphean Curse with Intrinsic Fear

DQN game Reinforcement Learning

#### Rainbow: Combining Improvements in Deep Reinforcement Learning

DQN game Reinforcement Learning

The deep reinforcement learning community has made several independent improvements to the DQN algorithm. However, it is unclear which of these extensions are complementary and can be fruitfully combined. This paper examines six extensions to the DQN algorithm and empirically studies their combinat…

#### Multi-Level Discovery of Deep Options

DQN game Reinforcement Learning

Augmenting an agent’s control with useful higher-level behaviors called options can greatly reduce the sample complexity of reinforcement learning, but manually designing options is infeasible in high-dimensional and abstract state spaces. While recent work has proposed several techniques for…

#### Deep Abstract Q-Networks

DQN game Reinforcement Learning

We examine the problem of learning and planning on high-dimensional domains with long horizons and sparse rewards. Recent approaches have shown great successes in many Atari 2600 domains. However, domains with long horizons and sparse rewards, such as Montezuma’s Revenge and Venture, remain c…

#### Learning to Drive using Inverse Reinforcement Learning and Deep Q-Networks

car DQN Reinforcement Learning

We propose an inverse reinforcement learning (IRL) approach using Deep Q-Networks to extract the rewards in problems with large state spaces. We evaluate the performance of this approach in a simulation-based autonomous driving scenario. Our results resemble the intuitive relation between the rewar…

#### Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents

The Arcade Learning Environment (ALE) is an evaluation platform that poses the challenge of building AI agents with general competency across dozens of Atari 2600 games. It supports a variety of different problem settings and it has been receiving increasing attention from the scientific community,…

#### The Uncertainty Bellman Equation and Exploration

DQN game Reinforcement Learning

We consider the exploration/exploitation problem in reinforcement learning. For exploitation, it is well known that the Bellman equation connects the value at any time-step to the expected value at subsequent time-steps. In this paper we consider a similar uncertainty Bellman equation (UBE), which …

#### Automated Cloud Provisioning on AWS using Deep Reinforcement Learning

As the use of cloud computing continues to rise, controlling cost becomes increasingly important. Yet there is evidence that 30% – 45% of cloud spend is wasted. Existing tools for cloud provisioning typically rely on highly trained human experts to specify what to monitor, thresholds for trig…

#### Pre-training Neural Networks with Human Demonstrations for Deep Reinforcement Learning

DNN DQN game Reinforcement Learning

Deep reinforcement learning (deep RL) has achieved superior performance in complex sequential tasks by using a deep neural network as its function approximator and by learning directly from raw images. A drawback of using raw images is that deep RL must learn the state feature representation from t…

#### Deep Reinforcement Learning with Surrogate Agent-Environment Interface

DQN gradient Reinforcement Learning

In this paper we propose surrogate agent-environment interface (SAEI) in reinforcement learning. We also state that learning based on probability surrogate agent-environment interface gives optimal policy of task agent-environment interface. We introduce surrogate probability action and develope th…

#### Autonomous Quadrotor Landing using Deep Reinforcement Learning

car DQN Reinforcement Learning

Landing an unmanned aerial vehicle on a ground marker is an open problem despite the effort of the research community. Previous attempts mostly focused on the analysis of hand-crafted geometric features and the use of external sensors in order to allow the vehicle to approach the land-pad. In this …

#### Formulation of Deep Reinforcement Learning Architecture Toward Autonomous Driving for On-Ramp Merge

ads car DQN LSTM Reinforcement Learning RNN

Multiple automakers have in development or in production automated driving systems (ADS) that offer freeway-pilot functions. This type of ADS is typically limited to restricted-access freeways only, that is, the transition from manual to automated modes takes place only after the ramp merging proce…

#### Deep Reinforcement Learning: An Overview

DNN DQN health image language Reinforcement Learning text

We give an overview of recent exciting achievements of deep reinforcement learning (RL). We discuss six core elements, six important mechanisms, and twelve applications. We start with background of machine learning, deep learning and reinforcement learning. Next we discuss core RL elements, includi…

#### OpenAI Baselines: DQN

We’re open-sourcing OpenAI Baselines, our internal effort to reproduce reinforcement learning algorithms with performance on par with published results. We’ll release the algorithms over upcoming months; today’s release includes DQN and three of its variants. Reinforcement learnin…