# Tag Info

6

This expression: $|\mathcal{A}(s)|$ means $|\quad|$ the size of $\mathcal{A}(s)$ the set of actions in state $s$ or more simply the number of actions allowed in the state. This makes sense in the given formula because $\frac{\epsilon}{|\mathcal{A}(s)|}$ is then the probability of taking each exploratory action in an $\epsilon$-greedy policy. The overall ...

5

Q-learning is guaranteed to converge (in the tabular case) under some mild conditions, one of which is that in the limit we visit each state-action tuple infinitely many times. If your random random policy (i.e. 100% exploration) is guaranteeing this and the other conditions are met (which they probably are) then Q-learning will converge. The reason that ...

4

DQN on the other hand, explores using epsilon greedy exploration. Either selecting the best action or a random action. This is a very common choice, because it is simple to implement and quite robust. However, it is not a requirement of DQN. You can use other action choice mechanisms, provided all choices are covered with a non-zero probability of being ...

4

The $\epsilon$-greedy policy is a policy that chooses the best action (i.e. the action associated with the highest value) with probability $1-\epsilon \in [0, 1]$ and a random action with probability $\epsilon$. The problem with $\epsilon$-greedy is that, when it chooses the random actions (i.e. with probability $\epsilon$), it chooses them uniformly (i.e. ...

3

The AlphaZero paper mentions an "evaluation" step that seems to deal with the the problem similar to yours: ... we evaluate each new neural network checkpoint against the current best network $f_{\theta_*}$ before using it for data generation ... Each evaluation consists of 400 games ... If the new player wins by a margin of > 55% (to avoid ...

3

Epsilon-greedy is one method of making an agent explore the state space to ensure that the agent doesn't settle on a sub-optimal policy. By taking random actions, even with a small probability, the agent can get to places in the state space it normally wouldn't see and on the chance that the outcome is better than what it normally would have seen, it can ...

2

The first thing to note here is that your results seem aligned with the results commonly found in the bandit literature. Second thing to note would be that the performance of bandit algorithms is usually measured in terms of regret. This is the difference between (i) the amount of rewards accumulated by an oracle policy having prior knowledge about the true ...

2

How much the $Q$-values change does not depend on the value of $\epsilon$, rather the value of $\epsilon$ dictates how likely you are to take a random action and thus take an action that could give rise to a large TD error -- that is a large difference between the returns you expected from taking this action as to what you actually observed. How much the $Q$-...

2

When in an environment with competing agents, from the perspective of each agent, the environment becomes non-markovian. That occurs because each agent is constantly adapting its own strategy to other's actions, so a transition that occurred to a pair (s,a) before, resulting in a positive reward, might result in zero or negative reward in future iterations ...

1

I read section 2.2 of Sutton and Barto, and I understand your confusion: the $\epsilon$-greedy algorithm is not defined precisely on page 27-28. Selecting an action randomly "every once in awhile" with probability $\epsilon$ means selecting an action randomly with probability $\epsilon$ at each timestep and selecting an action greedily with ...

1

Your graph looks to me like a typical learning curve plotted for training process in reinforcement learning. Looking at it in detail I can say: There is clearly some learning occurring. There is a strong random element throughout. As you say the epsilon is reduced to near zero by episode 2000, and I would assume a driving simulation is mostly deterministic ...

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What does it mean when ϵ=0 and ϵ=1? If ϵ=1, does it mean that the agent explores randomly? If this intuition is right, then it will not learn anything - right? On the other hand, if I set ϵ=0, does this imply that the agent doesn't explore? You are correct, when ϵ=1 the agent acts randomly. When ϵ=0, the agent always takes the current greedy actions. Both ...

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