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_e
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 key barrier
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...
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
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 ...
Q-Learning algorithm for off-policy TD control using Function Approximation. Added DQN script to run outside of notebook Sep 25, 2016 212 Finds the optimal greedy policy while following an epsilon-greedy policy. 213 214 Args: DQN updates Sep 26, 2016 215 sess: Tensorflow Se...
The PMR-Dueling DQN method for mobile robot path planning in complex and dynamic situations is used to address the problems of instability and slow convergence seen in the DQN algorithm. To achieve superior performance in terms of convergence speed, stability, and path planning performance, the alg...
The PMR-Dueling DQN method for mobile robot path planning in complex and dynamic situations is used to address the problems of instability and slow convergence seen in the DQN algorithm. To achieve superior performance in terms of convergence speed, stability, and path planning performance, the alg...
sensors Article Enhancing Stability and Performance in Mobile Robot Path Planning with PMR-Dueling DQN Algorithm Demelash Abiye Deguale 1,* , Lingli Yu 1 , Melikamu Liyih Sinishaw 2 and Keyi Li 1 1 School of Automation, Central South University, Changsha 410083, China; llyu@csu.edu.cn ...
microgrid; deep deterministic policy gradient algorithm; deep reinforcement learning; dueling DQN1. Introduction With the advancement of the new energy sector, the proportion of renewable energy sources is gradually increasing. In order to utilize these clean energy sources more efficiently and meet the...