4
votes
What is the relation between Dynamic Programming and Reinforcement Learning?
Dynamic programming is an algorithm paradigm (i.e. a way to design algorithms) that can be applied to many problem domains, not just Markov decision processes (MDPs), as long as they satisfy certain ...
- 39.5k
3
votes
Accepted
in simple words, what is the Q-learning algortimn steps?
This is taken from Sutton & Barto's RL book
...
- 239
2
votes
Accepted
Could someone give a very simple example of Q-learning in a very small environment?
one great library is called openAI gym which has a bunch of toy problems. One of these is CliffWalking based on Sutton and is a 3x12. This article steps through it pretty nicely and I am sure you can ...
- 239
2
votes
What happens when the probability of either one of the policies is 0 in Importance Sampling?
Background: Importance sampling is used in many off-policy RL algorithms when the data is generated with one policy, yet it is being used to update another policy. The policy generating the data is ...
- 1,282
1
vote
OpenAI Gym implementation of the delayed rewards
I think what you have here (with an important caveat, which I will get to later) is a common misunderstanding about how rewards should be structured for a reinforcement learning (RL) problem. It is ...
- 30.2k
1
vote
What happens when the probability of either one of the policies is 0 in Importance Sampling?
Very simply, one of the requirements of off-policy RL to converge, is that the behavioral policy $b$ has at least the same support of the target policy $\pi$, thus:
$$
\forall s \in S \forall a \in A \...
- 1,308
1
vote
RL agent for autonomous vehicle is able to follow the road but can't avoid crashing at all (Highway-Env / Racetrack Env.)
There are a few things I think worth looking into:
Is avoiding the other car actually possible in the environment? You may inadvertently be giving the agent a choice of crashing in lane, or going off ...
- 30.2k
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