Questions tagged [exploration-strategies]
For questions about exploration strategies (or techniques) used in reinforcement learning or bandit problems. Examples of exploration strategies are random strategy, $\epsilon$-greedy, greedy (no exploration), Upper Confidence Bound (UCB), or Thompson sampling.
12
questions
2
votes
1answer
38 views
Is it possible to apply a particular exploration policy for the on-policy RL agents?
Is it possible to use any particular strategy to explore (e.g. metaheuristics) in on-policy algorithms (e.g. in PPO) or is it only possible to define particular policies to explore in off-policy ...
1
vote
1answer
59 views
Which policy has to be followed by a player while construction of its own Q-table?
Consider the scenario, where there are two players. One of the players perform the action randomly, whereas I want second player as a Q-player. I mean, the player selects a best action from the Q-...
0
votes
1answer
69 views
Which policy do I need to use in updating Q function?
Policy function can be of two types: deterministic policy and stochastic policy.
Deterministic policy is of the form $\pi : S \rightarrow A$
Stochastic policy is defined using conditional probability ...
0
votes
0answers
22 views
(explore-exploit + supervised learning ) vs contextual bandits
Lets take an ad recommendation problem for 1 slot. Feedback is click/no click. I can solve this by contextual bandits. But I can also introduce exploration in supervised learning, I learn my model ...
1
vote
1answer
67 views
In addition to the reward function, which other functions do I need to implement Q-learning?
In general, $Q$ function is defined as
$$Q : S \times A \rightarrow \mathbb{R}$$
$$Q(s_t,a_t) = Q(s_t,a_t) + \alpha[r_{t+1} + \gamma \max\limits_{a} Q(s_{t+1},a) - Q(s_t,a_t)] $$
$\alpha$ and $\gamma$...
5
votes
1answer
223 views
Why does Q-learning converge under 100% exploration rate?
I am working on this assignment where I made the agent learn state-action values (Q-values) with Q-learning and 100% exploration rate. The environment is the classic gridworld as shown in the ...
5
votes
3answers
481 views
In Q-learning, wouldn't it be better to simply iterate through all possible states?
In Q-learning, all resources I've found seem to say that the algorithm to update the Q-table should start at some initial state, and pick actions (which are sometimes random) to explore the state ...
1
vote
0answers
78 views
Are actions deterministic during testing in continuous action space PPO?
In a continuous action space (for instance, in PPO, TRPO, REINFORCE, etc.), during training, an action is sampled from the random distribution with $\mu$ and $\sigma$. This results in an inherent ...
3
votes
1answer
164 views
Strategy for playing a board game with Minimax algorithm
I want to build a player for the following game:
You have a board where position 1 is your player, position 2 is the rival ...
3
votes
3answers
101 views
Why aren't exploration techniques, such as UCB or Thompson sampling, used in full RL problems?
Why aren't exploration techniques, such as UCB or Thompson sampling, typically used in bandit problems, used in full RL problems?
Monte Carlo Tree Search may use the above-mentioned methods in its ...
2
votes
1answer
67 views
In DQN, is it possible to make some actions more likely?
In a general DQN framework, if I have an idea of some actions being better than some other actions, is it possible to make the agent select the better actions more often?
5
votes
1answer
217 views
What is the relation between a policy which is the solution to a MDP and a policy like $\epsilon$-greedy?
In the context of reinforcement learning, a policy, $\pi$, is often defined as a function from the space of states, $\mathcal{S}$, to the space of actions, $\mathcal{A}$, that is, $\pi : \mathcal{S} \...
