Questions tagged [q-learning]

For questions related to the Q-learning algorithm, which is a model-free and temporal-difference reinforcement learning algorithm that attempts to approximate the Q function, which is a function that, given a state s and an action a, returns a real number that represents the return (or value) of state s when action a is taken from s. Q-learning was introduced in the PhD thesis "Learning from Delayed Rewards" (1989) by Watkins.

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1answer
1k views

How to use DQN to handle an imperfect but complete information game?

I'm currently having troubles to win against a random bot playing the Schieber Jass game. It is a imperfect card information game. (famous in switzerland https://www.schieber.ch/) The environement I'...
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0answers
301 views

Q-learning in Python

I'm working on a q-learning project that involves a "robot" solving a maze, and there is a problem with how I update the Q values (every time the robot ends up switching between two squares instead of ...
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2answers
309 views

Should the actor or actor-target model be used to make predictions after training is complete (DDPG)?

The situation I am referring to the paper T. P. Lillicrap et al, "Continuous control with deep reinforcement learning" where they discuss deep learning in the context of continuous action spaces ("...
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1answer
3k views

How to implement exploration function and learning rate in Q Learning

I'm trying to implement Q-learning (state-based representation and no neural / deep stuff) but I'm having a hard time getting it to learn anything. I believe my issue is with the exploration function ...
3
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1answer
477 views

Which Reinforcement Learning algorithms are efficient for episodic problems?

I have some episodic datasets extracted from a turn-based RTS game in which the current actions leading to the next state doesn’t determine the final solution/outcome of the episode. The learning is ...
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2answers
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Is Q-learning a type of model-based RL?

Model-based RL creates a model of the transition function. Tabular Q-Learning does this iteratively (without directly optimizing for the transition function). So, does this make tabular Q-learning a ...
5
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1answer
1k views

Why is the target $r + \gamma \max_{a'} Q(s', a'; \theta_i^-)$ in the loss function of the DQN architecture?

In the paper Human-level control through deep reinforcement learning, the DQN architecture is presented, where the loss function is as follows $$ L_i(\theta_i) = \mathbb{E}_{(s, a, r, s') \sim U(D)} \...
2
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1answer
90 views

Why is the access to the dynamics model unrealistic in Q-Learning?

Pieter Abbeel says that having access to the dynamics model, $P(s' \mid s,a)$, is unrealistic because it assumes we know the probability that we will reach all future states. I don't understand how ...
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0answers
172 views

Help with implementing Q-learning for a feedfoward network playing a video game

I want to train a feedforward neural network to play a video game called Puyo Puyo 2, using reinforcement learning. More specifically, I'm trying Q-learning but I'm open to better alternatives. In ...
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2answers
1k views

Q Learning Algorithm not converging

I am trying to run Deep Q-learning algorithm on a game which I made in Python using pygame library. The algorithm accepts the game screen (4 frames) as input to neural network which used as the ...
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1answer
2k views

What are other ways of handling invalid actions in scenarios where all rewards are either 0 (best reward) or negative?

I created an OpenAI Gym environment, and I would like to check the performance of the agent from OpenAI Baselines DQN approach on it. In my environment, the best possible outcome for the agent is 0 - ...
1
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1answer
135 views

Reinforce Learning: Do I have to ignore hyper parameter(?) after training done in Q-learning?

Learner might be in training stage, where it update Q-table for bunch of epoch. In this stage, Q-table would be updated with gamma(discount rate), learning rate(alpha), and action would be chosen by ...
2
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1answer
1k views

Q learning tic tac toe

I have a tic-tac-toe with a Q-learning algorithm, and the AI plays against the same algorithm (but they don't share the same Q matrix). But after 200,000 games, I still beat the AI very easily and it'...
4
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1answer
209 views

Why can't we apply value iteration when we do not know the reward and transition functions, and how does Q-learning solve this issue?

I don't understand why we can't apply value iteration when don't know the reward and transition probabilities. In this lecture, the lecturer says it has to do with not being able to take max with ...
4
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1answer
431 views

State representation of position in 2D plane for Reinforcement Learning (Q Learning)

I recently finished Course on RL by David Silver (on YT) and thought about trying it out on simple application in Unity Game Engine, where I've built simple labyrint with ball and want to teach the ...

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