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.
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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 ...
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3answers
463 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 ...
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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 ...
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1answer
124 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 ...
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3answers
85 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 ...
