PyTorch implementation of various reinforcement learning algorithms
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Updated
Feb 22, 2018 - Python
PyTorch implementation of various reinforcement learning algorithms
Reinforcement Learning For Dialogue Systems 强化学习在对话系统中的应用 论文或开源应用总结
Reinforcement learning algorithms to solve OpenAI gym environments
Various fundamental reinforcement learning algorithms implemented from scratch
Reinforcement Learning Specialization courses solutions
Reinforcement Learning Using Q-learning, Double Q-learning, and Dyna-Q.
path planning using Q learning algorithm
Implementation of Dyna-Q with priority sweeping on a basic n by n gridworld
Reinforcement Learning Algorithms in a simple Gridworld
ReLAx - Reinforcement Learning Applications Library
Assignments and Reading Material for RL Course
Solving Markov Decision Process using Value Iteration and Policy Iteration, SARSA, Expected SARSA and Q-Learning
Experiments with Dyna-Q
Reinforcement Learning: Modification of Q-learning through the use DynaQ learning and Double-Q learning.
Programming assignments completed for my Reinforcement Learning course: Topics include Bandit Algorithms, Dynamic Programming, policy iteration, Monte-Carlo methods, SARSA, Q-Learning, Dyna-Q/Dyna-Q+, gradient control methods, state aggregation methods, and Deep Q-Learning Networks (DQNs).
Example DYNA-Q implementation with ReLAx
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