Generative Adversarial Imagination for Sample Efficient Deep Reinforcement Learning

30 Apr 2019 Kacper Kielak

Reinforcement learning has seen great advancements in the past five years. The successful introduction of deep learning in place of more traditional methods allowed reinforcement learning to scale to very complex domains achieving super-human performance in environments like the game of Go or numerous video games... (read more)

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Methods used in the Paper


METHOD TYPE
Double Q-learning
Off-Policy TD Control
Prioritized Experience Replay
Replay Memory
Noisy Linear Layer
Randomized Value Functions
Dueling Network
Q-Learning Networks
N-step Returns
Value Function Estimation
Rainbow DQN
Q-Learning Networks
Q-Learning
Off-Policy TD Control
Dense Connections
Feedforward Networks
Convolution
Convolutions
DQN
Q-Learning Networks