mirror of
https://git.FreeBSD.org/ports.git
synced 2024-11-18 00:10:04 +00:00
22 lines
1.2 KiB
Plaintext
22 lines
1.2 KiB
Plaintext
OpenAI Gym is a toolkit for developing and comparing reinforcement learning
|
|
algorithms. This is the gym open-source library, which gives you access to a
|
|
standardized set of environments.
|
|
|
|
gym makes no assumptions about the structure of your agent, and is compatible
|
|
with any numerical computation library, such as TensorFlow or Theano. You can
|
|
use it from Python code, and soon from other languages.
|
|
|
|
There are two basic concepts in reinforcement learning: the environment (namely,
|
|
the outside world) and the agent (namely, the algorithm you are writing). The
|
|
agent sends actions to the environment, and the environment replies with
|
|
observations and rewards (that is, a score).
|
|
|
|
The core gym interface is Env, which is the unified environment interface. There
|
|
is no interface for agents; that part is left to you. The following are the Env
|
|
methods you should know:
|
|
- reset(self): Reset the environment's state. Returns observation.
|
|
- step(self, action): Step the environment by one timestep. Returns observation,
|
|
reward, done, info.
|
|
- render(self, mode='human'): Render one frame of the environment. The default
|
|
mode will do something human friendly, such as pop up a window.
|