Multi-Dimensional Deep Memory Atari-Go Players for Parameter Exploring Policy Gradients

By Mandy Grüttner, Frank Sehnke, Tom Schaul, J. Urgen Schmidhuber. 2010

14 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Developmental and Educational Psychology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. J. Urgen Schmidhuber
2. David R Ha
3. Alex Graves
4. Sergey Levine
5. Yoshua Bengio
6. Quoc V. Le
7. Faustino J. Gomez
8. Mohit Bansal

In Developmental and Educational Psychology

1. J. Urgen Schmidhuber

Citations in the corpus

• Deep learning in neural networks: An overview (2014, methodology): “Compare earlier RL Atari players (Grüttner et al., 2010) and an earlier, raw video-based RL NN for computer games (Koutnı́k et al.”
• On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models (2015, methodology): “1, except that C is now an FNN or RNN trained by evolutionary algorithms [200, 255, 105, 56, 68] applied to NNs [165, 321, 180, 259, 72, 90, 89, 110, 94], or by policy gradient methods [314, 315, 316, 274, 18, 1, 63, 128, 313, 210, 192, 191, 256, 85, 312, 190, 82, 93][245, Sec.”
• One Big Net For Everything (2018, background): “6.6], or reinforcement learning [29, 90, 101, 76] through policy gradients [104, 91, 3, 1, 13, 30, 103, 48, 82, 22, 102, 42] [76, Sec.”