The Atari 2600 Games task (and dataset) involves training an agent to achieve high game scores.
( Image credit: Playing Atari with Deep Reinforcement Learning )
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Humans integrate multiple sensory modalities (e. g. visual and audio) to build a causal understanding of the physical world.
Unsupervised extraction of objects from low-level visual data is an important goal for further progress in machine learning.
In this paper, we first characterize the convergence rate for Q-AMSGrad, which is the Q-learning algorithm with AMSGrad update (a commonly adopted alternative of Adam for theoretical analysis).
It has been arduous to assess the progress of a policy learning algorithm in the domain of hierarchical task with high dimensional action space due to the lack of a commonly accepted benchmark.
Model-based reinforcement learning (RL) is appealing because (i) it enables planning and thus more strategic exploration, and (ii) by decoupling dynamics from rewards, it enables fast transfer to new reward functions.
In neuroscience, attention has been shown to bidirectionally interact with reinforcement learning (RL) processes.
Experience replay enables online reinforcement learning agents to store and reuse the experiences generated in previous interaction with the environment.