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Skip to Search Results- 4Reinforcement Learning
- 2Real-Time Learning
- 1Alternate policy gradient estimator
- 1Application of Reinforcement Learning
- 1Control
- 1Deep Reinforcement Learning
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Spring 2022
Policy gradient (PG) estimators are ineffective in dealing with softmax policies that are sub-optimally saturated, which refers to the situation when the policy concentrates its probability mass on sub-optimal actions. Sub-optimal policy saturation may arise from a bad policy initialization or a...
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Fall 2023
Off-policy policy evaluation has been a critical and challenging problem in reinforcement learning, and Temporal-Difference (TD) learning is one of the most important approaches for addressing it. There has been significant interest in searching for off-policy TD algorithms which find the same...
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Spring 2023
Vision is one of the essential means for humans to perceive the world. Similarly, today's intelligent robot agents rely on camera images to perform complex tasks in the real world. Due to the ever-changing nature of the real world, intelligent robot agents must continually learn from...
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Fall 2021
Continuous-time reinforcement learning tasks commonly use discrete time steps of fixed cycle times for actions. Choosing a small action-cycle time in such tasks allows reinforcement learning agents fast reaction and a more temporally detailed perception of the environment. The learning...