Monitors

keras_gym.wrappers.TrainMonitor

Environment wrapper for monitoring the training process.

class keras_gym.wrappers.TrainMonitor(env, tensorboard_dir=None)

Environment wrapper for monitoring the training process.

This wrapper logs some diagnostics at the end of each episode and it also gives us some handy attributes (listed below).

Parameters:

env : gym environment

A gym environment.

tensorboard_dir : str, optional

If provided, TrainMonitor will log all diagnostics to be viewed in tensorboard. To view these, point tensorboard to the same dir:

$ tensorboard --logdir {tensorboard_dir}

Attributes:

T : positive int

Global step counter. This is not reset by env.reset(), use env.reset_global() instead.

ep : positive int

Global episode counter. This is not reset by env.reset(), use env.reset_global() instead.

t : positive int

Step counter within an episode.

G : float

The return, i.e. amount of reward accumulated from the start of the current episode.

avg_G : float

The average return G, averaged over the past 100 episodes.

dt_ms : float

The average wall time of a single step, in milliseconds.

close(self)

Override close in your subclass to perform any necessary cleanup.

Environments will automatically close() themselves when garbage collected or when the program exits.

record_losses(self, losses)

Record losses during the training process.

These are used to print more diagnostics.

Parameters:

losses : dict

A dict of losses/metrics, of type {name <str>: value <float>}.

render(self, mode='human', **kwargs)

Renders the environment.

The set of supported modes varies per environment. (And some environments do not support rendering at all.) By convention, if mode is:

• human: render to the current display or terminal and return nothing. Usually for human consumption.
• rgb_array: Return an numpy.ndarray with shape (x, y, 3), representing RGB values for an x-by-y pixel image, suitable for turning into a video.
• ansi: Return a string (str) or StringIO.StringIO containing a terminal-style text representation. The text can include newlines and ANSI escape sequences (e.g. for colors).

Note:

Make sure that your class’s metadata ‘render.modes’ key includes

the list of supported modes. It’s recommended to call super() in implementations to use the functionality of this method.

Args:

mode (str): the mode to render with

Example:

class MyEnv(Env):

metadata = {‘render.modes’: [‘human’, ‘rgb_array’]}

def render(self, mode=’human’):

if mode == ‘rgb_array’:

return np.array(…) # return RGB frame suitable for video

elif mode == ‘human’:

… # pop up a window and render

else:

super(MyEnv, self).render(mode=mode) # just raise an exception

reset(self)

Resets the state of the environment and returns an initial observation.

Returns:

observation (object): the initial observation.

reset_global(self)

Reset the global counters, not just the episodic ones.

seed(self, seed=None)

Sets the seed for this env’s random number generator(s).

Note:

Some environments use multiple pseudorandom number generators. We want to capture all such seeds used in order to ensure that there aren’t accidental correlations between multiple generators.

Returns:

list<bigint>: Returns the list of seeds used in this env’s random

number generators. The first value in the list should be the “main” seed, or the value which a reproducer should pass to ‘seed’. Often, the main seed equals the provided ‘seed’, but this won’t be true if seed=None, for example.

step(self, a)

Run one timestep of the environment’s dynamics. When end of episode is reached, you are responsible for calling reset() to reset this environment’s state.

Accepts an action and returns a tuple (observation, reward, done, info).

Args:

action (object): an action provided by the agent

Returns:

observation (object): agent’s observation of the current environment reward (float) : amount of reward returned after previous action done (bool): whether the episode has ended, in which case further step() calls will return undefined results info (dict): contains auxiliary diagnostic information (helpful for debugging, and sometimes learning)

unwrapped

Completely unwrap this env.

Returns:

gym.Env: The base non-wrapped gym.Env instance