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Fall 2022
In most, if not every, realistic sequential decision-making tasks, the decision-making agent is not able to model the full complexity of the world. In reinforcement learning, the environment is often much larger and more complex than the agent, a setting also known as partial observability. In...
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Fall 2021
Reinforcement learning (RL) is a learning paradigm focusing on how agents interact with an environment to maximize cumulative reward signals emitted from the environment. Exploration versus exploitation challenge is critical in RL research: the agent ought to trade off between taking the known...
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Fall 2022
Actor-Critics are a popular class of algorithms for control. Their ability to learn complex behaviours in continuous-action environments make them directly applicable to many real-world scenarios. These algorithms are composed of two parts - a critic and an actor. The critic learns to critique...
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Fall 2023
The transformer architecture is effective in processing sequential data, both because of its ability to leverage parallelism, and because of its self-attention mechanism capable of capturing long-range dependencies. However, the self-attention mechanism is slow for streaming data, that is when...