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- 3Reinforcement Learning
- 1Adaptive Decision-making
- 1Artificial Intelligence
- 1Auxiliary Tasks
Reinforcement Learning is a formalism for learning by trial and error. Unfortunately, trial and error can take a long time to find a solution if the agent does not efficiently explore the behaviours available to it. Moreover, how an agent ought to explore depends on the task that the agent is...
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...
Learning auxiliary tasks, such as multiple predictions about the world, can provide many benets to reinforcement learning systems. A variety of off-policy learning algorithms have been developed to learn such predictions, but as yet there is little work on how to adapt the behavior to gather...
The performance of reinforcement learning (RL) agents is sensitive to the choice of hyperparameters. In real-world settings like robotics or industrial control systems, however, testing different hyperparameter configurations directly on the environment can be financially prohibitive, dangerous,...
In this thesis, we investigate different vector step-size adaptation approaches for continual, online prediction problems. Vanilla stochastic gradient descent can be considerably improved by scaling the update with a vector of appropriately chosen step-sizes. Many methods, including AdaGrad,...