Exploring the Limitations of Behavior Cloning for Autonomous Driving

Driving requires reacting to a wide variety of complex environment conditions and agent behaviors. Explicitly modeling each possible scenario is unrealistic. In contrast, imitation learning can, in theory, leverage data from large fleets of human-driven cars. Behavior cloning in particular has been successfully used to learn simple visuomotor policies end-to-end, but scaling to the full spectrum of driving behaviors remains an unsolved problem. In this paper, we propose a new benchmark to experimentally investigate the scalability and limitations of behavior cloning. We show that behavior cloning leads to state-of-the-art results, including in unseen environments, executing complex lateral and longitudinal maneuvers without these reactions being explicitly programmed. However, we confirm well-known limitations (due to dataset bias and overfitting), new generalization issues (due to dynamic objects and the lack of a causal model), and training instability requiring further research before behavior cloning can graduate to real-world driving. The code of the studied behavior cloning approaches can be found at https://github.com/felipecode/coiltraine

MAGDA: A Mobile Agent based Grid Architecture

Mobile agents mean both a technology and a programming paradigm. They allow for a flexible approach which can alleviate a number of issues present in distributed and Grid-based systems, by means of features such as migration, cloning, messaging and other provided mechanisms. In this paper we describe an architecture (MAGDA – Mobile Agent based Grid Architecture) we have designed and we are currently developing to support programming and execution of mobile agent based application upon Grid systems

Evaluating the relation between changeability decay and the characteristics of clones and methods

In this paper we propose a methodology to evaluate if there is a relation between two code characteristics. The methodology is based on relative risk, an epidemiology formula used to analyze the effect of toxic agents in developing diseases. We present a metaphor in which the disease is changeability decay, measured at method level, and the toxic agent is a source code characteristic considered harmful. However, the formula assesses the strength of the relation between any toxic agent and any disease. We apply the methodology to explore cloning as a toxic agent that increases the risk of changeability decay. Cloning is a good agent to analyze given that although there is some evidence of maintainability issues caused by clones, we do not know which clones are harmful, or to what extent. We compare cloning with other possible 'toxic agents', like having high complexity or having high fan-in. We also use the technique to evaluate which clone characteristics (like clone size) may indicate harmful clones, by testing such characteristics as toxic agents. We found that cloning is one of the method characteristics that affects the least changeability decay, and that none of the clone characteristics analyzed are related with changeability decay

Generative Multi-Agent Behavioral Cloning

We propose and study the problem of generative multi-agent behavioral cloning, where the goal is to learn a generative, i.e., non-deterministic, multi-agent policy from pre-collected demonstration data. Building upon advances in deep generative models, we present a hierarchical policy framework that can tractably learn complex mappings from input states to distributions over multi-agent action spaces by introducing a hierarchy with macro-intent variables that encode long-term intent. In addition to synthetic settings, we show how to instantiate our framework to effectively model complex interactions between basketball players and generate realistic multi-agent trajectories of basketball gameplay over long time periods. We validate our approach using both quantitative and qualitative evaluations, including a user study comparison conducted with professional sports analysts

Behaviourally cloning river raid agents

We investigate the feasibility and difficulties of using behavioural cloning to obtain player models using the 1982 video game River Raid. We attempt to clone both virtual game-playing agents (a fixed (non-improving) reinforcement learning agent and a random agent sampling actions uniformly) as well as an actual human agent. The behavioural clones' performance is evaluated on the micro-level through comparison of the state-conditioned and unconditional action distributions, and on the macro-level by comparing the (cloned) agents' survival time and score per episode. Using our methodology, cloning virtual agents seems feasible to varying extents, even with somewhat limited amounts of data. However, our method fails to create reliable behavioural clones of human players. We conclude with a discussion of some of the more important reasons that might cause this: a lack of training data, the problem of covariate shift, and improving and inconsistent play-style over time

Expression capable library for studies of Neisseria gonorrhoeae, version 1.0

Background The sexually transmitted disease, gonorrhea, is a serious health problem in developed as well as in developing countries, for which treatment continues to be a challenge. The recent completion of the genome sequence of the causative agent, Neisseria gonorrhoeae, opens up an entirely new set of approaches for studying this organism and the diseases it causes. Here, we describe the initial phases of the construction of an expression-capable clone set representing the protein-coding ORFs of the gonococcal genome using a recombination-based cloning system. Results The clone set thus far includes 1672 of the 2250 predicted ORFs of the N. gonorrhoeae genome, of which 1393 (83%) are sequence-validated. Included in this set are 48 of the 61 ORFs of the gonococcal genetic island of strain MS11, not present in the sequenced genome of strain FA1090. L-arabinose-inducible glutathione-S-transferase (GST)-fusions were constructed from random clones and each was shown to express a fusion protein of the predicted size following induction, demonstrating the use of the recombination cloning system. PCR amplicons of each ORF used in the cloning reactions were spotted onto glass slides to produce DNA microarrays representing 2035 genes of the gonococcal genome. Pilot experiments indicate that these arrays are suitable for the analysis of global gene expression in gonococci. Conclusion This archived set of Gateway® entry clones will facilitate high-throughput genomic and proteomic studies of gonococcal genes using a variety of expression and analysis systems. In addition, the DNA arrays produced will allow us to generate gene expression profiles of gonococci grown in a wide variety of conditions. Together, the resources produced in this work will facilitate experiments to dissect the molecular mechanisms of gonococcal pathogenesis on a global scale, and ultimately lead to the determination of the functions of unknown genes in the genome

Controlled Secret Sharing Protocol using a Quantum Cloning Circuit

We demonstrate the possibility of controlling the success probability of a secret sharing protocol using a quantum cloning circuit. The cloning circuit is used to clone the qubits containing the encoded information and {\em en route} to the intended receipients. The success probability of the protocol depends on the cloning parameters used to clone the qubits. We also establish a relation between the concurrence of initially prepared state, entanglement of the mixed state received by the receivers after cloning scheme and the cloning parameters of cloning machine.Comment: This is a modified version of the previous work quant-ph/arXiv:1011.286