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Learning more skills through optimistic exploration

arXiv.org Artificial Intelligence

Unsupervised skill learning objectives (Gregor et al., 2016, Eysenbach et al., 2018) allow agents to learn rich repertoires of behavior in the absence of extrinsic rewards. They work by simultaneously training a policy to produce distinguishable latent-conditioned trajectories, and a discriminator to evaluate distinguishability by trying to infer latents from trajectories. The hope is for the agent to explore and master the environment by encouraging each skill (latent) to reliably reach different states. However, an inherent exploration problem lingers: when a novel state is actually encountered, the discriminator will necessarily not have seen enough training data to produce accurate and confident skill classifications, leading to low intrinsic reward for the agent and effective penalization of the sort of exploration needed to actually maximize the objective. To combat this inherent pessimism towards exploration, we derive an information gain auxiliary objective that involves training an ensemble of discriminators and rewarding the policy for their disagreement. Our objective directly estimates the epistemic uncertainty that comes from the discriminator not having seen enough training examples, thus providing an intrinsic reward more tailored to the true objective compared to pseudocount-based methods (Burda et al., 2019). We call this exploration bonus discriminator disagreement intrinsic reward, or DISDAIN. We demonstrate empirically that DISDAIN improves skill learning both in a tabular grid world (Four Rooms) and the 57 games of the Atari Suite (from pixels). Thus, we encourage researchers to treat pessimism with DISDAIN.


Microsoft acquires AI company to make Cortana and bots sound more human

#artificialintelligence

Microsoft is acquiring conversational AI startup Semantic Machines in an effort to make bots and intelligent assistants like Cortana sound and respond more like humans. Founded in 2014, Semantic Machines uses machine learning to make bots respond in a more natural way to queries. Semantic Machines is led by UC Berkeley professor Dan Klein and former Apple chief speech scientist Larry Gillick. Both are considered pioneers in conversational AI. Microsoft's acquisition will boost the company's Cortana digital assistant, as well as the company's Azure Bot Service that's used by 300,000 developers.