About us
some stuff here.
def myfun():
return a
more things about the code, now with autodic. $$ this is an equation? $$ or this $a=b$ or what about :math:a^2=b this.
Go see the class irlc.ex01.agent.Agent for extra stuff.
- class irlc.ex01.agent.Agent(env)[source]
Main agent class. See (Her21, Subsection 4.4.3) for additional details.
- Example
>>> print("Hello World") "Hello world"
\begin{eqnarray} y & = & ax^2 + bx + c \\ f(x) & = & x^2 + 2xy + y^2 \end{eqnarray}- extra_stats()[source]
Optional: Can be used to record extra information from the Agent while training. You can safely ignore this method, it will only be used for control theory to create nicer plots
- hello(greeting)[source]
The canonical hello world example.
A longer description with some RST.
- Parameters
greeting (
str) – The person to say hello to.- Returns
The greeting
- Return type
str
- 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
- train(s, a, r, sp, done=False)[source]
Called at each step of the simulation after a = pi(s,k) and environment transition to sp.
Allows the agent to learn from experience
- Parameters
s – Current state x_k
a – Action taken
r – Reward obtained by taking action a_k in x_k
sp – The state that the environment transitioned to \({\bf x}_{k+1}\)
done – Whether environment terminated when transitioning to sp
- Returns
None