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Abstraction-Guided Modular Reinforcement Learning
C.T. Ponnambalam
Algorithmics
Research output
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Thesis
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Dissertation (TU Delft)
131
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INIS
learning
100%
humans
61%
policy
46%
solutions
38%
data
30%
information
15%
neural networks
15%
recovery
15%
space
15%
parallel processing
7%
applications
7%
people
7%
trains
7%
performance
7%
tools
7%
environment
7%
Computer Science
Reinforcement Learning
100%
Model
44%
Recovery Policy
22%
Information Gathering
11%
Supervised Learning
11%
Predicted State
11%
State Space
11%
Complex Function
11%
Deep Neural Network
11%
Complexity
11%
Application
11%
Presented Approach
11%
Learning Agent
11%
Parallel Processing
11%
Neural Network
11%
Conceptual Understanding
11%
Human Learning
11%
Learning Process
11%
Knowledge Transfer
11%
Keyphrases
Modular Reinforcement Learning
100%
Fundamental Strength
20%
Meta-agents
20%
Reward Maximization
20%