Seq2Seq Mimic Games: A Signaling Perspective
arXiv:1811.06564
Abstract
We study the emergence of communication in multiagent adversarial settings inspired by the classic Imitation game. A class of three player games is used to explore how agents based on sequence to sequence (Seq2Seq) models can learn to communicate information in adversarial settings. We propose a modeling approach, an initial set of experiments and use signaling theory to support our analysis. In addition, we describe how we operationalize the learning process of actor-critic Seq2Seq based agents in these communicational games.
arXiv commentNIPS 2018 Workshop on Emergent Communication (accepted)
References in corpus (14)
- Adam: A Method for Stochastic Optimization
- Sequence to Sequence Learning with Neural Networks
- Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
- Categorical Reparameterization with Gumbel-Softmax
- A Neural Conversational Model
- Learning to Communicate with Deep Multi-Agent Reinforcement Learning
- SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
- GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution
- Adversarial Evaluation of Dialogue Models
- Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols
- Emergent Communication through Negotiation
- Data Generation as Sequential Decision Making
- Stochastic Backpropagation through Mixture Density Distributions
- Proximal Policy Optimization and its Dynamic Version for Sequence Generation