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1answer
14 views

Using a combination of spatial and non-spatial inputs for convolutional neural networks

I'm working on training a game AI using deep reinforcement learning to achieve specific examples based on pixel input and some additional state information. Naturally, I'm using a convolutional ...
2
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1answer
24 views

How is the environment designed for testing a reinforcement learning algorithm?

I'm working on a project, and have a candidate algorithm which I'd like to test. Before I go any further, I need to get the hang of how to code the "structure" of the environment in which my system is ...
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7 views

Dyna-Q in non-deterministic domains

I've implemented the Dyna-Q reinforcement learning algorithm and it works perfectly on a discrete deterministic environment, the cliff. However, when applying it to a continuous environment (mountain ...
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1answer
27 views

Apprenticeship vs. imitation learning - what is the difference?

There are many sub-domains of reinforcement learning, two of which are apprenticeship and imitation learning - but that appears just to be two different names for the same thing?
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0answers
19 views

Middle ground between model-based and model-free approaches

In the context of the reinforcement learning domain, the dichotomy between model-based (learn a model and used it to determine a controller) and model-free (learn a controller without learning a ...
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1answer
19 views

What does it mean to have a continuous action space w.r.t. to reinforcement learning?

Last time I posted this question I got criticised for not being specific enough, hence this is my second attempt at trying to understand what it means to have a continuous action space. Please refer ...
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1answer
26 views

single agent vs multiple agent reinforcement learning

I am confused about 'single' vs 'multiple' agent reinforcement learning. Let's say that I have 1 hunter who I am training to hunt 1 static prey, so that only the hunter is moving around. This is ...
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0answers
56 views

Reinforcement Learning - Agent training

In RL, in a game situation, usually the agent is trained by playing against itself. When we should not depend on this self-training, and switch to train the agent with a real or different player ...
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0answers
13 views

Memory storage capacity of Bienenstock-Cooper-Munro rule

I would like to know the memory storage capacity of the BCM learning rule when it is implemented on a Hopfield network. I understand that it will be a function of n where n is the number of neurons.
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1answer
30 views

Combining Production Rules using Reinforcement Learning

Production systems have been used to solve puzzles such as the Tower of Hanoi for years with hard-coded production rules. However, has there been any research in using reinforcement learning to ...
5
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1answer
55 views

The meaning of discount factor on reinforcement learning

After reading of the google deepmind achievements on Atari's games, I am trying to understand the q-learning and q-networks, but I am little bit confused. The confusion arise in the concept of the ...
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0answers
11 views

Reward function equivalence classes

Background: I have a reinforcement learning problem in which agents are learning how to interpret the knowledge given to them by other agents. The details of how they do this are a bit involved so ...
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0answers
28 views

How to determine convergence when using Q-learning?

I'm using Q-Learning to find the values of states in on a gameboard. For example, something like: ...
2
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1answer
48 views

How does SARSA handle episode termination

When applied to domains that are episodic and have a "final" state but no final action, like a game, how does SARSA update the Q-values? e.g. A game agent would receive this series: $$ ...
5
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1answer
191 views

What's the difference between Adaptive Control and Hierarchical Reinforcement Learning?

After watching Travis DeWolf presentation on scaling neural computation, I'm a bit confused about the difference between Reinforcement Learning (whether hierarchical or not) and Adaptive Control. They ...
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0answers
40 views

Reinforcement learning - state space and action space

I am working on a reinforcement learning strategy for parameter control of a local search heuristic. The complete state for this RL problem can be defined as $S = \{s, p\}$, where $s$ and $p$ ...
3
votes
1answer
109 views

Why does ε-greedy $Q$-learning not oscillate?

I have a intuitive question on the convergence of $Q$- learning. In $Q$ learning for each step a $Q$- value is learned for the state-action pair where the action is selected according to the ...
2
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1answer
198 views

Q-learning in a Dynamic environment

I am new to reinforcement learning. Lately, I have learned Q-learning using the following tutorial. Is Q-learning still possible if the environment is dynamic. Using the environment of the tutorial ...