75,925 research outputs found
: Transferring Visual Representations for Reinforcement Learning via Prompting
It is important for deep reinforcement learning (DRL) algorithms to transfer
their learned policies to new environments that have different visual inputs.
In this paper, we introduce Prompt based Proximal Policy Optimization
(), a three-stage DRL algorithm that transfers visual representations
from a target to a source environment by applying prompting. The process of
consists of three stages: pre-training, prompting, and predicting. In
particular, we specify a prompt-transformer for representation conversion and
propose a two-step training process to train the prompt-transformer for the
target environment, while the rest of the DRL pipeline remains unchanged. We
implement and evaluate it on the OpenAI CarRacing video game. The
experimental results show that outperforms the state-of-the-art visual
transferring schemes. In particular, allows the learned policies to
perform well in environments with different visual inputs, which is much more
effective than retraining the policies in these environments.Comment: This paper has been accepted to be presented at the upcoming IEEE
International Conference on Multimedia & Expo (ICME) in 202
Deep learning for video game playing
In this article, we review recent Deep Learning advances in the context of
how they have been applied to play different types of video games such as
first-person shooters, arcade games, and real-time strategy games. We analyze
the unique requirements that different game genres pose to a deep learning
system and highlight important open challenges in the context of applying these
machine learning methods to video games, such as general game playing, dealing
with extremely large decision spaces and sparse rewards
Human-Agent Decision-making: Combining Theory and Practice
Extensive work has been conducted both in game theory and logic to model
strategic interaction. An important question is whether we can use these
theories to design agents for interacting with people? On the one hand, they
provide a formal design specification for agent strategies. On the other hand,
people do not necessarily adhere to playing in accordance with these
strategies, and their behavior is affected by a multitude of social and
psychological factors. In this paper we will consider the question of whether
strategies implied by theories of strategic behavior can be used by automated
agents that interact proficiently with people. We will focus on automated
agents that we built that need to interact with people in two negotiation
settings: bargaining and deliberation. For bargaining we will study game-theory
based equilibrium agents and for argumentation we will discuss logic-based
argumentation theory. We will also consider security games and persuasion games
and will discuss the benefits of using equilibrium based agents.Comment: In Proceedings TARK 2015, arXiv:1606.0729
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