Action space
An action space is the set or structured specification of actions available to an agent in a decision problem or environment.
Evolutionary Deep Learning: Genetic algorithms and neural networks
An action space specifies the set and structure of actions that an agent can take in an environment.
- How it is used
- Defines the choices sampled during exploration or scored during Q-learning and DQN interaction with Gym environments.
- How deep it goes
- Explains discrete action counts through Q-table dimensions, epsilon-greedy selection, and DQN output sizing; defers Q-function detail.
- Examples
- Uses FrozenLake, MountainCar, CartPole, Acrobot, CubeCrash, and LunarLander Gym environments plus random-agent simulations.
- What you can do after
- Apply it via Gym to inspect valid actions, sample exploratory moves, and size Q-tables or DQN output layers.
Read how Evolutionary Deep Learning: Genetic algorithms and neural networks explains action space →
Optimization Algorithms: AI techniques for design, planning, and control problems
An action space is the set of actions available to an agent in an environment.
- How it is used
- Defines the permissible choices an agent can make while learning a policy through environment interaction.
- How deep it goes
- Distinguishes discrete and continuous actions and shows how a discrete action count configures a policy, without detailing space validation.
- Examples
- Uses board-game and robot-control contrasts, then works through CartPole’s left and right actions and actor-network output.
- What you can do after
- Apply it via Gym by reading the action-space size and matching the policy output to the environment’s available actions.
Related concepts
- neuroevolution of augmenting topologiesconfigures · NeuroEvolution of Augmenting Topologies (NEAT) is an evolutionary method that evolves neural-network weights and connection topologies, typically through genomes and speciation.
- tool usageconfigured by · Tool use is an AI system's invocation of external software functions, services, or other capabilities to obtain information or perform actions.
- environmentpart of · An environment is the external context, including its entities and conditions, in which a system, process, or agent exists or operates.
- Q-tableconfigured by · A Q-table is a lookup table storing estimated action values for state-action pairs in a tabular reinforcement-learning system.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.