q-learning

问题 I've made a simple version of Curve Fever also known as "Achtung Die Kurve". I want the machine to figure out how to play the game optimally. I copied and slightly modified an existing DQN from some Atari game examples that is made with Google's Tensorflow. I'm tyring to figure out an appropriate reward function. Currently, I use this reward setup: 0.1 for every frame it does not crash -500 for every crash Is this the right approach? Do I need to tweak the values? Or do I need a completely

问题 I've been doing Deep Reinforcement Learning using Tensorflow and OpenAI gym. My problem is low GPU utilisation. Googling this issue, I understood that it's wrong to expect much GPU utilisation when training small networks ( eg. for training mnist). But my Neural Network is not so small, I think. The architecture is similar to the given in the original deepmind paper (more or less). The architecture of my network is summarized below Convolution layer 1 (filters=32, kernel_size=8x8, strides=4)

问题 I've been doing Deep Reinforcement Learning using Tensorflow and OpenAI gym. My problem is low GPU utilisation. Googling this issue, I understood that it's wrong to expect much GPU utilisation when training small networks ( eg. for training mnist). But my Neural Network is not so small, I think. The architecture is similar to the given in the original deepmind paper (more or less). The architecture of my network is summarized below Convolution layer 1 (filters=32, kernel_size=8x8, strides=4)

问题 Given that the OpenAI Gym environment MountainCar-v0 ALWAYS returns -1.0 as a reward (even when goal is achieved), I don't understand how DQN with experience-replay converges, yet I know it does, because I have working code that proves it. By working, I mean that when I train the agent, the agent quickly (within 300-500 episodes) learns how to solve the mountaincar problem. Below is an example from my trained agent. It is my understanding that ultimately there needs to be a "sparse reward"

问题 So I plugged QLearningDiscreteDense into a dots and boxes game I made. I created a custom MDP environment for it. The problem is that it chooses action 0 each time, the first time it works but then it's not an available action anymore so it's an illegal move. I give illegal moves a reward of Integer.MIN_VALUE , but it doesn't affect anything. Here's the MDP class: public class testEnv implements MDP<testState, Integer, DiscreteSpace> { final private int maxStep; DiscreteSpace actionSpace =

问题 So I plugged QLearningDiscreteDense into a dots and boxes game I made. I created a custom MDP environment for it. The problem is that it chooses action 0 each time, the first time it works but then it's not an available action anymore so it's an illegal move. I give illegal moves a reward of Integer.MIN_VALUE , but it doesn't affect anything. Here's the MDP class: public class testEnv implements MDP<testState, Integer, DiscreteSpace> { final private int maxStep; DiscreteSpace actionSpace =

来源： https://stackoverflow.com/questions/64794996/incompatible-array-types-are-mixed-in-the-forward-input-linearfunction-in-mach

来源： https://stackoverflow.com/questions/64794996/incompatible-array-types-are-mixed-in-the-forward-input-linearfunction-in-mach

来源： https://stackoverflow.com/questions/64794996/incompatible-array-types-are-mixed-in-the-forward-input-linearfunction-in-mach

来源： https://stackoverflow.com/questions/61526437/deep-q-learning-without-openai-gym