irlc.ex01.agent.Agent.pi
- Agent.pi(s, k=None)[source]
Evaluate the Agent’s policy at time step k in state s
The details will differ depending on whether the agent interacts in a discrete-time or continous-time setting.
For discrete application (dynamical programming/search and reinforcement learning), k is discrete k=0, 1, 2, …
For control applications, k is continious and denote simulation time t, i.e. it should be called as
> agent.pi(x, t)
- Parameters
s – Current state
k – Current time index.
- Returns
action