Last call to make your voice heard! Our 2022 Developer Survey closes in less than a week. Take survey.

# Tag Info

Accepted

### What algorithms are considered reinforcement learning algorithms?

The dynamic programming algorithms (like policy iteration and value iteration) are often presented in the context of reinforcement learning (in particular, in the book Reinforcement Learning: An ...
• 33.2k
Accepted

### Why does TD Learning require Markovian domains?

The Markov assumption is used when deriving the Bellman equation for state values: $$v(s) = \sum_a \pi(a|s)\sum_{r,s'} p(r,s'|s,a)(r + \gamma v(s'))$$ One requirement for this equation to hold is that ...
• 23.3k

### What algorithms are considered reinforcement learning algorithms?

In Reinforcement Learning: An Introduction the authors suggest that the topic of reinforcement learning covers analysis and solutions to problems that can be framed in this way: Reinforcement ...
• 23.3k
Accepted

### Why is update rule of the value function different in policy evaluation and policy iteration?

Yes, the two update equations are equivalent. As an aside, technically the equation you give is not the Bellman equation, but the update step re-written as an equation - in the Bellman equation ...
• 23.3k
Accepted

### Do we need the transition probability function when calculating the importance sampling ratio?

There is one thing I don't particularly understand. Why do we need the state-transition probability function when calculating the importance sampling ratio for off-policy prediction? It is not needed ...
• 23.3k
Accepted

### Should we feed a greater fraction of terminal states to the value network so that their values are learned first?

The basis of Q-learning is recursive (similar to dynamic programming), where only the absolute value of the terminal state is known. This may be true in some environments. Many environments do not ...
• 23.3k
Accepted

### What are the common techniques one could use to deal with collisions in a transposition table?

The term you're looking for is "replacement schemes". As far as I'm aware, the primary reference on this is still Replacement Schemes for Transposition Tables, although it is a fairly old paper from ...
• 9,336
Accepted

You can obtain the optimal policy from the optimal state value function if you also have the state transition and reward model for the environment $p(s',r|s,a)$ - the probability of receiving reward $... • 23.3k 2 votes Accepted ### How do we get from conditional expectation on both state and action to only state in the proof of the Policy Improvement Theorem? I don't understand how did we get rid of the condition$A_{t}=\pi'(s)$. We don't really, it is just moved into the subscript$\pi'$in$\mathbb{E}_{\pi'}[]$- it means the same thing here, that the ... • 23.3k 2 votes Accepted ### Are policy and value iteration used only in grid world like scenarios? Policy and value iteration both require you to, for each possible transition and each corresponding possible reward at each state, compute a statistic of$r + \gamma V(s')$. In order for this to be ... • 961 1 vote ### How are these two versions of the Bellman optimality equation related? My guess is that$r(s,a)$is the constant so it can be moved out of the summation, leaving$r(s,a)\sum_{s'}P^{a}_{ss'} = r(s,a)$Yes, this is the case. More specifically:$r(s,a)$is the expected ... • 23.3k 1 vote ### Is there a notion of exploration-exploitation tradeoff in dynamic programming (or model-based RL)? I think there is an implicit notion of it in dynamic programming; say, if you have to make some sort of search over a subset of a state space and you are deciding whether to use BFS, breath first ... • 121 1 vote Accepted ### How do we get the value of this state of an MDP, at time-step$h-2\$, using dynamic programming?

Wow, that's a really confusing example, if I were you I would check out some other RL resources. I wouldn't consider h being the last step and h-1 being the previous step. In terms of steps of ...
• 221
1 vote
Accepted

### Bellman optimality equation in semi Markov decision process

The core problem here is state representation, not estimating return due to delayed response to actions on the original state representation (which is no longer complete for the new problem). If you ...
• 23.3k
1 vote

### If the transition model is available, why would we use sample-based algorithms?

A full Bellman update can be intractable. For instance, if your state space or action space are continuous, the full Bellman update is intractable. You can try to solve this by discretizing, but if ...
• 961
1 vote

### What algorithms are considered reinforcement learning algorithms?

It seems that another rather controversial point is about the inclusion of evolutionary algorithms as Reinforcement Learning ones. Sutton & Barto do not. They argue that And also: Other people ...
1 vote

### Should we feed a greater fraction of terminal states to the value network so that their values are learned first?

If you have enough domain knowledge to be able to reliably, intentionally reach those terminal states often when generating experience, yeah, that could help. Generally, the assumption in ...
• 9,336

Only top scored, non community-wiki answers of a minimum length are eligible