Cyber resilience using autonomous agents and reinforcement learning

2020 ◽  
Author(s):  
Hasan Cam
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Agents

scholarly journals A strategy learning model for autonomous agents based on classification

2015 ◽  
Vol 25 (3) ◽  
pp. 471-482 ◽  
Author(s):  
Bartłomiej Śnieżyński
Keyword(s):  
Reinforcement Learning ◽  
Supervised Learning ◽  
Learning Process ◽  
Autonomous Agents ◽  
Good Alternative ◽  
Learning Model ◽  
Learning Method ◽  
Complex Environments ◽  
Agent Based ◽  
Proposed Model

AbstractIn this paper we propose a strategy learning model for autonomous agents based on classification. In the literature, the most commonly used learning method in agent-based systems is reinforcement learning. In our opinion, classification can be considered a good alternative. This type of supervised learning can be used to generate a classifier that allows the agent to choose an appropriate action for execution. Experimental results show that this model can be successfully applied for strategy generation even if rewards are delayed. We compare the efficiency of the proposed model and reinforcement learning using the farmer-pest domain and configurations of various complexity. In complex environments, supervised learning can improve the performance of agents much faster that reinforcement learning. If an appropriate knowledge representation is used, the learned knowledge may be analyzed by humans, which allows tracking the learning process

scholarly journals What Can You Do with a Rock? Affordance Extraction via Word Embeddings

2017 ◽  
Author(s):  
Nancy Fulda ◽  
Daniel Ricks ◽  
Ben Murdoch ◽  
David Wingate
Keyword(s):  
Reinforcement Learning ◽  
Computational Complexity ◽  
Linear Algebra ◽  
Autonomous Agents ◽  
Common Knowledge ◽  
Search Space ◽  
Word Embeddings ◽  
Knowledge Database ◽  
Learning Agent ◽  
Action Spaces

Autonomous agents must often detect affordances: the set of behaviors enabled by a situation. Affordance extraction is particularly helpful in domains with large action spaces, allowing the agent to prune its search space by avoiding futile behaviors. This paper presents a method for affordance extraction via word embeddings trained on a tagged Wikipedia corpus. The resulting word vectors are treated as a common knowledge database which can be queried using linear algebra. We apply this method to a reinforcement learning agent in a text-only environment and show that affordance-based action selection improves performance in most cases. Our method increases the computational complexity of each learning step but significantly reduces the total number of steps needed. In addition, the agent's action selections begin to resemble those a human would choose.

scholarly journals Active Inference: Demystified and Compared

2021 ◽  
pp. 1-39
Author(s):  
Noor Sajid ◽  
Philip J. Ball ◽  
Thomas Parr ◽  
Karl J. Friston
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Agents ◽  
Arm Movement ◽  
Generative Models ◽  
Active Inference ◽  
Discrete State ◽  
Q Learning ◽  
Learning Agents ◽  
Time Formulation ◽  
Complex Settings

Active inference is a first principle account of how autonomous agents operate in dynamic, nonstationary environments. This problem is also considered in reinforcement learning, but limited work exists on comparing the two approaches on the same discrete-state environments. In this letter, we provide (1) an accessible overview of the discrete-state formulation of active inference, highlighting natural behaviors in active inference that are generally engineered in reinforcement learning, and (2) an explicit discrete-state comparison between active inference and reinforcement learning on an OpenAI gym baseline. We begin by providing a condensed overview of the active inference literature, in particular viewing the various natural behaviors of active inference agents through the lens of reinforcement learning. We show that by operating in a pure belief-based setting, active inference agents can carry out epistemic exploration—and account for uncertainty about their environment—in a Bayes-optimal fashion. Furthermore, we show that the reliance on an explicit reward signal in reinforcement learning is removed in active inference, where reward can simply be treated as another observation we have a preference over; even in the total absence of rewards, agent behaviors are learned through preference learning. We make these properties explicit by showing two scenarios in which active inference agents can infer behaviors in reward-free environments compared to both Q-learning and Bayesian model-based reinforcement learning agents and by placing zero prior preferences over rewards and learning the prior preferences over the observations corresponding to reward. We conclude by noting that this formalism can be applied to more complex settings (e.g., robotic arm movement, Atari games) if appropriate generative models can be formulated. In short, we aim to demystify the behavior of active inference agents by presenting an accessible discrete state-space and time formulation and demonstrate these behaviors in a OpenAI gym environment, alongside reinforcement learning agents.

Enabling Artificial Intelligence Studies in Off-Road Mobility Through Physics-based Simulation of Multi-Agent Scenarios

2021 ◽  
Author(s):  
Aaron Young ◽  
Jay Taves ◽  
Asher Elmquist ◽  
Simone Benatti ◽  
Alessandro Tasora ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Vehicles ◽  
Message Passing ◽  
Message Passing Interface ◽  
Autonomous Agents ◽  
Time Space ◽  
Intelligence Studies ◽  
Multi Agent ◽  
Physics Based Simulation ◽  
Design And Testing

Abstract We describe a simulation environment that enables the design and testing of control policies for off-road mobility of autonomous agents. The environment is demonstrated in conjunction with the training and assessment of a reinforcement learning policy that uses sensor fusion and inter-agent communication to enable the movement of mixed convoys of human-driven and autonomous vehicles. Policies learned on rigid terrain are shown to transfer to hard (silt-like) and soft (snow-like) deformable terrains. The environment described performs the following: multi-vehicle multibody dynamics co-simulation in a time/space-coherent infrastructure that relies on the Message Passing Interface standard for low-latency parallel computing; sensor simulation (e.g., camera, GPU, IMU); simulation of a virtual world that can be altered by the agents present in the simulation; training that uses reinforcement learning to 'teach' the autonomous vehicles to drive in an obstacle-riddled course. The software stack described is open source.

scholarly journals Autonomous robotic nanofabrication with reinforcement learning

2020 ◽  
Vol 6 (36) ◽  
pp. eabb6987 ◽  
Author(s):  
Philipp Leinen ◽  
Malte Esders ◽  
Kristof T. Schütt ◽  
Christian Wagner ◽  
Klaus-Robert Müller ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Self Assembly ◽  
Autonomous Agents ◽  
Single Molecules ◽  
Building Blocks ◽  
Supramolecular Structures ◽  
Atomic Scale ◽  
Solution Strategies ◽  
The Face ◽  
Robotic Construction

The ability to handle single molecules as effectively as macroscopic building blocks would enable the construction of complex supramolecular structures inaccessible to self-assembly. The fundamental challenges obstructing this goal are the uncontrolled variability and poor observability of atomic-scale conformations. Here, we present a strategy to work around both obstacles and demonstrate autonomous robotic nanofabrication by manipulating single molecules. Our approach uses reinforcement learning (RL), which finds solution strategies even in the face of large uncertainty and sparse feedback. We demonstrate the potential of our RL approach by removing molecules autonomously with a scanning probe microscope from a supramolecular structure. Our RL agent reaches an excellent performance, enabling us to automate a task that previously had to be performed by a human. We anticipate that our work opens the way toward autonomous agents for the robotic construction of functional supramolecular structures with speed, precision, and perseverance beyond our current capabilities.

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