the amount of EVs has been increasing rapidly [2,3]. However, the lack of convenient charging infrastructure and low effective charging scheduling algorithm for large-scale EVs charging scheduling becomes the ke
There are two main phases that are interleaved in the Deep Q-Learning Algorithm. One is where we sample the environment by performing actions and store away the observed experienced tuples in a replay memory. The other is where we select the small batch of tuples from this memory, randomly...
In the last unit, we learned our first reinforcement learning algorithm: Q-Learning,implemented it from scratch, and trained it in two environments, FrozenLake-v1 ☃️ and Taxi-v3 🚕. We got excellent results with this simple algorithm. But these environments were relatively simple becau...
During the use of the Double-DQN algorithm, this paper defines state values, action values, and reward values that are suitable for the redundant battery balancing circuit. Once the agent learns the optimal policy, it is deployed to the switch controller. Finally, an experimental platform is ...
replay_memory_size=500000, replay_memory_init_size=50000, update_target_estimator_every=10000, discount_factor=0.99, epsilon_start=1.0, epsilon_end=0.1, epsilon_decay_steps=500000, batch_size=32, record_video_every=50): """ Q-Learning algorithm for off-policy TD control using Function Approxi...
In this section, we first analyze the principle of federated learning, and then, present dueling DQN algorithm with novel action exploration for the microgrid MDP model. Consequently, combing the advantage of federated learning and the improved dueling DQN, the federated DDQN based microgrid energy...
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course. - reinforcement-learning/DQN/dqn.py at master · dennybritz/reinforcement-learning
The Deep Q-Learning Algorithm We learned that Deep Q-Learninguses a deep neural network to approximate the different Q-values for each possible action at a state(value-function estimation). The difference is that, during the training phase, instead of updating the Q-value of a state-action...
The Deep Q-Learning Algorithm We learned that Deep Q-Learninguses a deep neural network to approximate the different Q-values for each possible action at a state(value-function estimation). The difference is that, during the training phase, instead of updating the Q-value of a stat...
The Deep Q-Learning Algorithm We learned that Deep Q-Learninguses a deep neural network to approximate the different Q-values for each possible action at a state(value-function estimation). The difference is that, during the training phase, instead of updating the Q-value of a state...