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Timothy Lillicrap
Elements of DSAI: Game Tree Search, Learning Architectures
Discrete Sequential Prediction of Continu
Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning
Improving the Gating Mechanism of Recurrent
Bayesian Policy Selection Using Active Inference
Meta-Learning Deep Energy-Based Memory Models
Shixiang (Shane) Gu
Deep Learning Without Weight Transport
Arxiv:2010.02193V3 [Cs.LG] 3 May 2021
A General Reinforcement Learning Algorithm That Masters Chess, Shogi, and Go Through Self-Play
A 20-Year Community Roadmap for Artificial Intelligence Research in the US
A Brief Survey of Deep Reinforcement Learning Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, Anil Anthony Bharath
Deep Reinforcement Learning at Scale
Towards Biologically Plausible Gradient Descent by Jordan
Activation Relaxation: a Local Dynamical Approximation To
Shixiang (Shane) Gu
Alpha Zero Paper
CSC 311: Introduction to Machine Learning Lecture 12 - Alphago and Game-Playing
Top View
A General Reinforcement Learning Algorithm That Masters Chess, Shogi and Go Through Self-Play
Tutorial Workshop on Contemporary Deep Neural Network Models
Continuous Control with Deep Reinforcement Learning
Playing Nondeterministic Games Through Planning with a Learned
Relevance Realization and the Emerging Framework in Cognitive Science
Alireza Makhzani
Metalearned Neural Memory
Human Trust Modeling for Bias Mitigation in Artificial Intelligence
Arxiv:1811.04551V5 [Cs.LG] 4 Jun 2019 1
Object Exchangeability in Reinforcement Learning
Sample-Efficient Reinforcement Learning with Stochastic Ensemble
Matching Networks for One Shot Learning
Meta-Learning with Memory-Augmented Neural Networks
Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
Value Targets in Off-Policy Alphazero: a New Greedy Backup
Alphazero: Shedding New Light on Chess, Shogi, and Go | Deepmind 2/24/20, 9:25 PM
Metalearned Neural Memory
Three-Head Neural Network Architecture for Alphazero Learning
A Very Condensed Survey and Critique of Multiagent Deep Reinforcement Learning JAAMAS Track
Perspectivesin Format As Provided by the Authors
Introducing Failure Ratio in Reinforcement Learning
Tuning Recurrent Neural Networks with Re
Continual and Multi-Task Reinforcement Learning With
Symbolic Behaviour in Artificial Intelligence
Generalisation of Structural Knowledge in the Hippocampal-Entorhinal System
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm Arxiv:1712.01815V1 [Cs.AI] 5 Dec 2017
The Department of Defense Posture for Artificial Intelligence
Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures
Human Trust Modeling for Bias Mitigation in Artificial Intelligence
A Learning Gap Between Neuroscience and Reinforcement Learning
A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning
Next-Generation of Recurrent Neural Network Models for Cognition
Deep Quality-Value (DQV) Learning
Reinforcement Learning with Latent Flow
Deep Learning Without Weight Transport
Deep Learning Needs a Prefrontal Cortex
Relational Recurrent Neural Networks
Arxiv:1912.01603V3 [Cs.LG] 17 Mar 2020