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Q-learning
WebDec 13, 2024 · A Python realization of Q-learning; Q-learning exploration policy with ε-greedy; TD and Q-learning are quite important in RL because a lot of optimized methods are … WebFletcher McClellan’s Post Fletcher McClellan Professor of Political Science at Elizabethtown College inanimate object essay
Building a Checkers Gaming Agent Using Deep Q-Learning
Q-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring adaptations. For any finite Markov decision … See more Reinforcement learning involves an agent, a set of states $${\displaystyle S}$$, and a set $${\displaystyle A}$$ of actions per state. By performing an action $${\displaystyle a\in A}$$, the agent transitions from … See more Learning rate The learning rate or step size determines to what extent newly acquired information overrides old information. A factor of 0 makes the agent learn nothing (exclusively exploiting prior knowledge), while a factor of 1 makes the … See more Q-learning was introduced by Chris Watkins in 1989. A convergence proof was presented by Watkins and Peter Dayan in 1992. Watkins was … See more The standard Q-learning algorithm (using a $${\displaystyle Q}$$ table) applies only to discrete action and state spaces. Discretization of … See more After $${\displaystyle \Delta t}$$ steps into the future the agent will decide some next step. The weight for this step is calculated as See more Q-learning at its simplest stores data in tables. This approach falters with increasing numbers of states/actions since the likelihood … See more Deep Q-learning The DeepMind system used a deep convolutional neural network, with layers of tiled convolutional filters to mimic the effects of receptive … See more WebDeep Q-learning is identical to tabular or linear Q-learning, except that we use a deep neural network to represent the Q-function instead of a Q-table or a linear equation. ... Deep Q-learning offers alternatives in which we do not need to select features, but requires more training data (more episodes) and has no convergence guarantees ... WebOct 20, 2024 · SARSA is a value-based method similar to Q-learning. Hence, it uses a Q-table to store values for each state-action pair. With value-based strategies, we train the agent … inanimate object horror movies