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Results tagged with reinforcement-learning
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user 30431
For questions related to reinforcement learning, i.e. a machine learning technique where we imagine an agent that interacts with an environment (composed of states) in time steps by taking actions and receiving rewards (or reinforcements), then, based on these interactions, the agent tries to find a policy (i.e. a behavioural strategy) that maximizes the cumulative reward (in the long run), so the goal of the agent is to maximize the reward.
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How does reinforcement learning with video data work?
My goal is to train an agent to play MarioKart on the Nintendo DS. My first approach (in theory) was to setup an emulator on my pc and let the agent play for ages. But then a colleague suggested to tr …
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How to represent a state in a card game environment? (Wizard)
We are attempting to build an AI that manages to play the cardgame Wizard. So far er have a working network (based on the YOLO object-detection) that is abled to detect which cards are played. When as …
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How should I design the action space of an agent that needs to choose a 2d point and then sh...
I'm building a game environment (see the picture below) where an agent should position the mouse on the screen (see the coordinates on the upper right corner) and then click to shoot a cannonball. If …
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Why is this deep Q agent constantly learning just one action?
I'm trying to implement deep q learning in the OpenAI's gym "Taxi-v3" environment. But my agent only learns to do one action in every state. What am I doing wrong? Here is the Github repository with t …
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Accepted
Why is this deep Q agent constantly learning just one action?
I thought about my input-layer. I had the 500 states one hot encoded. So 499 of every input node would be 0. And 0 is very bad in an neural network. I tried the same code with the "CardPole-v0" and it …
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What is the difference between batches in deep Q learning and supervised learning?
How is the batch loss calculated in both DQNs and simple classifiers? From what I understood, in a classifier, a common method is that you sample a mini-batch, calculate the loss for every example, ca …
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How can a DQN backpropagate its loss?
I'm currently trying to take the next step in deep learning. I managed so far to write my own basic feed-forward network in python without any frameworks (just numpy and pandas), so I think I understo …
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