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Skip to Search Results- 2Reward Shaping
- 1Artificial Intelligence
- 1Machine Learning
- 1Model-Based Reinforcement Learning
- 1Planning
- 1Reinforcement Learning
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Fall 2024
Planning and goal-conditioned reinforcement learning aim to create more efficient and scalable methods for complex, long-horizon tasks. These approaches break tasks into manageable subgoals and leverage prior knowledge to guide learning. However, learned models may predict inaccurate next states...
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Spring 2020
Reinforcement learning (RL) is a powerful learning paradigm in which agents can learn to maximize sparse and delayed reward signals. Although RL has had many impressive successes in complex domains, learning can take hours, days, or even years of training data. A major challenge of contemporary...