All Questions
Tagged with proofs q-learning
7 questions
2
votes
1
answer
282
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What is the derivative of equation 1 in the paper "Conservative Q-Learning for Offline Reinforcement Learning"?
I am looking at the paper Conservative Q-Learning for Offline Reinforcement Learning, but I'm not sure how they proved theorem 3.1.
Here is a screenshot of theorem 3.1.
In the proof of theorem 3.1
...
- 121
8
votes
0
answers
293
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Is the Bellman equation that uses sampling weighted by the Q values (instead of max) a contraction?
It is proved that the Bellman update is a contraction (1).
Here is the Bellman update that is used for Q-Learning:
$$Q_{t+1}(s, a) = Q_{t}(s, a) + \alpha*(r(s, a, s') + \gamma \max_{a^*} (Q_{t}(s',
...
- 81
5
votes
1
answer
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Proof of Maximization Bias in Q-learning?
In the textbook "Reinforcement Learning: An Introduction" by Richard Sutton and Andrew Barto, the concept of Maximization Bias is introduced in section 6.7, and how Q-learning "over-estimates" action-...
- 157
22
votes
3
answers
6k
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Why doesn't Q-learning converge when using function approximation?
The tabular Q-learning algorithm is guaranteed to find the optimal $Q$ function, $Q^*$, provided the following conditions (the Robbins-Monro conditions) regarding the learning rate are satisfied
$\...
- 41.4k
3
votes
2
answers
593
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Why is the max a non-expansive operator?
In certain reinforcement learning (RL) proofs, the operators involved are assumed to be non-expansive. For example, on page 6 of the paper Generalized Markov Decision Processes: Dynamic-programming ...
- 41.4k
11
votes
2
answers
2k
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How do we prove the n-step return error reduction property?
In section 7.1 (about the n-step bootstrapping) of the book Reinforcement Learning: An Introduction (2nd edition), by Andrew Barto and Richard S. Sutton, the authors write about what they call the "n-...
- 111
2
votes
1
answer
2k
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Why does Q-learning converge to the optimal policy, even if the agent acts sub-optimally?
In Q-learning, during training, it doesn't matter how the agent selects actions. The algorithm always converges to the optimal policy. Why does this happen? What's the intuition?