actionselection

Action selection is the process by which an agent chooses an action to perform given its current state and knowledge. It is a central component of decision making in artificial agents and is typically implemented as part of a policy or a decision rule. In reinforcement learning and related frameworks, action selection determines how the agent translates state information and learned estimates into behavior.

In value-based approaches, the agent maintains estimates of the value of state-action pairs and uses these estimates to select actions. In policy-based approaches, the agent directly defines a policy that maps states to action probabilities. Some methods combine both ideas, using a value function to guide a stochastic policy.

Common action-selection strategies include:

- Greedy: always selecting the action with the highest estimated value.

- Epsilon-greedy: with probability epsilon, select a random action; otherwise act greedily.

- Softmax (Boltzmann): choose actions with probabilities proportional to an exponentiated value, enabling graded exploration.

- Upper Confidence Bound (UCB): select actions by balancing estimated value with uncertainty, encouraging exploration of less tried actions.

- Thompson sampling: a Bayesian approach that samples from the posterior distribution over action values.

- Deterministic versus stochastic policies: some tasks favor fixed rules, others rely on randomness to explore.

Action selection is concerned with the exploration-exploitation trade-off, robustness to noise, and efficiency in learning. It adapts to tasks such as robotics, game playing, and recommendation systems, where environments may be uncertain or changing. Implementations often use approximations of value or policy in high-dimensional or continuous action spaces. The effectiveness of action selection depends on the accuracy of value estimates, the chosen exploration strategy, and the dynamics of the environment.