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- Non-stationary Reinforcement Learning without Prior Knowledge . . .
Specif-ically, we propose a general approach that is applicable to various reinforcement learning settings (including bandits, episodic MDPs, infinite-horizon MDPs, etc ) and achieves optimal dynamic re-gret without any prior knowledge on the degree of non-stationarity
- Reinforcement Learning Algorithms and Use Cases - Coursera
Reinforcement learning algorithms allow artificial intelligence agents to learn the optimal way to perform a task through trial and error without human intervention Explore reinforcement learning algorithms such as Q-learning and actor-critic
- Reinforcement Learning – Overview of recent progress and . . .
A Reinforcement Learning agent has the goal of learning the best way to accomplish a task through repeated interactions with its environment (Sutton and Barto, 2018) In order to accomplish this the agent must evaluate the long-term value of the actions that it takes
- Preserving and combining knowledge in robotic lifelong . . .
Here we introduce a robotic lifelong reinforcement learning framework that addresses this gap by developing a knowledge space inspired by the Bayesian non-parametric domain In addition, we
- Interactive Reinforcement Learning with Dynamic Reuse of . . .
DRoP leverages the demonstrator’s knowledge by automatically balancing between reusing the prior knowledge and the current learned policy, allow-ing the agent to outperform the original demon-strations We compare with multiple state-of-the-art learning algorithms and empirically show that DRoP can achieve superior performance in two do-mains
- Non-stationary Reinforcement Learning without Prior Knowledge . . .
We propose a black-box reduction that turns a certain reinforcement learning algorithm with optimal regret in a (near-)stationary environment into another algorithm with optimal dynamic regret in a non-stationary environment, importantly without any prior knowledge on the degree of non-stationarity
- Emerging Strategies in Reinforcement Learning Methods - Springer
We discuss how these cutting-edge techniques are pushing the boundaries of what’s possible in RL, enabling more efficient learning, better generalization, and application to increasingly complex real-world problems
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