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- 3Reinforcement Learning
- 1Artificial Intelligence
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- 1Ensemble methods
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Spring 2020
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...
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Spring 2019
In the reinforcement learning (RL) problem an agent must learn how to act optimally through trial-and-error interactions with a complex, unknown, stochastic environment. The actions taken by the agent influence not just the immediate reward it observes but also the future states and rewards it...
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Improving Deep Deterministic Policy Gradient for Sparse Reward and Goal-Conditioned Continuous Control
DownloadSpring 2024
We propose an improved version of deep deterministic policy gradient (DDPG) for sparse reward and goal-conditioned reinforcement learning. To enhance exploration, we introduce \emph{${\epsilon}{t}$-greedy}, which uses search to generate exploratory options, focusing on less-visited states. We...
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Sample-Efficient Control with Directed Exploration in Discounted MDPs Under Linear Function Approximation
DownloadSpring 2022
An important goal of online reinforcement learning algorithms is efficient data collection to learn near-optimal behaviour, that is, optimizing the exploration-exploitation trade-off to reduce the sample-complexity of learning. To improve sample-complexity of learning it is essential that the...
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Spring 2016
How can the principles and concepts applied by visual communication designers be used to assist in exploring and understanding the massive, complex volumes of data now available to Digital Humanities researchers? One method we might employ to help us more easily comprehend the implications of...
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Spring 2024
Optimistic value estimates provide one mechanism for directed exploration in reinforcement learning (RL). The agent acts greedily with respect to an estimate of the value plus what can be seen as a value bonus. The value bonus can be learned by estimating a value function on reward bonuses,...