
Investigating Reinforcement Learning Agents for Continuous State Space Environments
Given an environment with continuous state spaces and discrete actions, ...
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ContinuousDiscrete Reinforcement Learning for Hybrid Control in Robotics
Many realworld control problems involve both discrete decision variable...
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Learning Action Representations for Reinforcement Learning
Most modelfree reinforcement learning methods leverage state representa...
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Lifelong Learning with a Changing Action Set
In many realworld sequential decision making problems, the number of av...
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A GoalBased Movement Model for Continuous MultiAgent Tasks
Despite increasing attention paid to the need for fast, scalable methods...
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Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning
In an effort to better understand the different ways in which the discou...
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Deep Reinforcement Learning with Discrete Normalized Advantage Functions for Resource Management in Network Slicing
Network slicing promises to provision diversified services with distinct...
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Deep Reinforcement Learning in Large Discrete Action Spaces
Being able to reason in an environment with a large number of discrete actions is essential to bringing reinforcement learning to a larger class of problems. Recommender systems, industrial plants and language models are only some of the many realworld tasks involving large numbers of discrete actions for which current methods are difficult or even often impossible to apply. An ability to generalize over the set of actions as well as sublinear complexity relative to the size of the set are both necessary to handle such tasks. Current approaches are not able to provide both of these, which motivates the work in this paper. Our proposed approach leverages prior information about the actions to embed them in a continuous space upon which it can generalize. Additionally, approximate nearestneighbor methods allow for logarithmictime lookup complexity relative to the number of actions, which is necessary for timewise tractable training. This combined approach allows reinforcement learning methods to be applied to largescale learning problems previously intractable with current methods. We demonstrate our algorithm's abilities on a series of tasks having up to one million actions.
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