125 research outputs found
Generating Personas for Games with Multimodal Adversarial Imitation Learning
Reinforcement learning has been widely successful in producing agents capable
of playing games at a human level. However, this requires complex reward
engineering, and the agent's resulting policy is often unpredictable. Going
beyond reinforcement learning is necessary to model a wide range of human
playstyles, which can be difficult to represent with a reward function. This
paper presents a novel imitation learning approach to generate multiple persona
policies for playtesting. Multimodal Generative Adversarial Imitation Learning
(MultiGAIL) uses an auxiliary input parameter to learn distinct personas using
a single-agent model. MultiGAIL is based on generative adversarial imitation
learning and uses multiple discriminators as reward models, inferring the
environment reward by comparing the agent and distinct expert policies. The
reward from each discriminator is weighted according to the auxiliary input.
Our experimental analysis demonstrates the effectiveness of our technique in
two environments with continuous and discrete action spaces.Comment: Published in CoG 202
Toward evolutionary and developmental intelligence
Given the phenomenal advances in artificial intelligence in specific domains like visual object recognition and game playing by deep learning, expectations are rising for building artificial general intelligence (AGI) that can flexibly find solutions in unknown task domains. One approach to AGI is to set up a variety of tasks and design AI agents that perform well in many of them, including those the agent faces for the first time. One caveat for such an approach is that the best performing agent may be just a collection of domain-specific AI agents switched for a given domain. Here we propose an alternative approach of focusing on the process of acquisition of intelligence through active interactions in an environment. We call this approach evolutionary and developmental intelligence (EDI). We first review the current status of artificial intelligence, brain-inspired computing and developmental robotics and define the conceptual framework of EDI. We then explore how we can integrate advances in neuroscience, machine learning, and robotics to construct EDI systems and how building such systems can help us understand animal and human intelligence
Boosting Reinforcement Learning and Planning with Demonstrations: A Survey
Although reinforcement learning has seen tremendous success recently, this
kind of trial-and-error learning can be impractical or inefficient in complex
environments. The use of demonstrations, on the other hand, enables agents to
benefit from expert knowledge rather than having to discover the best action to
take through exploration. In this survey, we discuss the advantages of using
demonstrations in sequential decision making, various ways to apply
demonstrations in learning-based decision making paradigms (for example,
reinforcement learning and planning in the learned models), and how to collect
the demonstrations in various scenarios. Additionally, we exemplify a practical
pipeline for generating and utilizing demonstrations in the recently proposed
ManiSkill robot learning benchmark
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