During training of our algorithm we set targets for gradient descend as: We see that the target depends on the current network. A neural network works as a whole, and so each update to a point in the Q function also influences whole area around that point. And the points of Q(s, a)...
The next piece of the training loop updates the main neural network using the SGD algorithm by minimizing the loss: optimizer.zero_grad()loss_t.backward()optimizer.step() Finally, the last line of the code syncs parameters from our main DQN network to the target DQN network every sync_targ...
After Connect Four, I wanted to try expanding the algorithm on games with more than 2 players and also games that didn't have perfect information, and so I chose Incan Gold. To my surprise, the overall observation and action space is smaller than Connect Four! I then wanted to try workin...
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...
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 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...
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...
This algorithm has the characteristics of ensuring convergence under certain conditions and has become the most widely used RL algorithm. When using Q-learning, the agent selects a action in the current state of the environment. After the environment receives the action, it will update to a ...
The AI server is configured with the DQN algorithm and PK-DQN algorithm for data training, which is transmitted to the DL server after training to form a deep neural network model. The cloud server transmits the original confrontation data and action sequence to the AI server, and receives ...