4 citations · 5 across the 7 of their papers we have counts for
7 papers
Unbiased Weight Maximization
Stephen Chung
A biologically plausible method for training an Artificial Neural Network (ANN) involves treating each unit as a stochastic Reinforcement Learning (RL) agent, thereby considering t…
Structural Credit Assignment with Coordinated Exploration
Stephen Chung
A biologically plausible method for training an Artificial Neural Network (ANN) involves treating each unit as a stochastic Reinforcement Learning (RL) agent, thereby considering t…
Thinker: Learning to Plan and Act
Stephen Chung, Ivan Anokhin, David Krueger
We propose the Thinker algorithm, a novel approach that enables reinforcement learning agents to autonomously interact with and utilize a learned world model. The Thinker algorithm…
Domain Generalization for Robust Model-Based Offline Reinforcement Learning
Alan Clark, Shoaib Ahmed Siddiqui, Robert Kirk +3
Existing offline reinforcement learning (RL) algorithms typically assume that training data is either: 1) generated by a known policy, or 2) of entirely unknown origin. We consider…
Learning by Competition of Self-Interested Reinforcement Learning Agents
Stephen Chung
An artificial neural network can be trained by uniformly broadcasting a reward signal to units that implement a REINFORCE learning rule. Though this presents a biologically plausib…
MAP Propagation Algorithm: Faster Learning with a Team of Reinforcement Learning Agents
Stephen Chung
Nearly all state-of-the-art deep learning algorithms rely on error backpropagation, which is generally regarded as biologically implausible. An alternative way of training an artif…
Reinforcement Learning with Feedback-modulated TD-STDP
Stephen Chung, Robert Kozma
Spiking neuron networks have been used successfully to solve simple reinforcement learning tasks with continuous action set applying learning rules based on spike-timing-dependent…