all.presets

all.presets

• all.presets.atari
• all.presets.classic_control
• all.presets.continuous

class all.presets.IndependentMultiagentPreset(name, device, presets)

Bases: Preset

agent(logger=<all.logging.dummy.DummyLogger object>, train_steps=inf)

Instantiate a training-mode Agent with the existing model.

Parameters:

• logger (all.logging.Logger, optional) – Coefficient for the entropy term in the total loss.

• train_steps (int, optional) – The number of steps for which the agent will be trained.

Returns:

The instantiated Agent.

Return type:

all.agents.Agent

test_agent()

Instansiate a test-mode Agent with the existing model.

Returns:

The instantiated test Agent.

Return type:

all.agents.Agent

class all.presets.ParallelPreset(name, device, hyperparameters)

Bases: ABC

A Preset ParallelAgent factory.

This is the ParallelAgent version of all.presets.Preset. This class allows the user to instantiate preconfigured ParallelAgents and test Agents. All Agents constructed by the ParallelPreset share a network model and parameters. However, other objects, such as ReplayBuffers, are independently created for each Agent. The ParallelPreset can be saved and loaded from disk.

abstract agent(logger=None, train_steps=inf)

Instantiate a training-mode ParallelAgent with the existing model.

Parameters:

• logger (all.logging.Logger, optional) – Coefficient for the entropy term in the total loss.

• train_steps (int, optional) – The number of steps for which the agent will be trained.

Returns:

The instantiated Agent.

Return type:

all.agents.ParallelAgent

property n_envs

abstract parallel_test_agent()

Instantiate a test-mode ParallelAgent with the existing model. See also: ParallelPreset.test_agent()

Returns:

The instantiated test ParallelAgent.

Return type:

all.agents.ParallelAgent

save(filename)

Save the preset and the contained model to disk.

The preset can later be loaded using torch.load(filename), allowing a test mode agent to be instantiated for evaluation or other purposes.

Parameters:

filename (str) – The path where the preset should be saved.

abstract test_agent()

Instantiate a test-mode Agent with the existing model. See also: ParallelPreset.parallel_test_agent()

Returns:

The instantiated test Agent.

Return type:

all.agents.Agent

class all.presets.ParallelPresetBuilder(default_name, default_hyperparameters, constructor, device='cuda', env=None, hyperparameters=None, name=None)

Bases: PresetBuilder

build()

class all.presets.Preset(name, device, hyperparameters)

Bases: ABC

A Preset Agent factory.

This class allows the user to instantiate preconfigured Agents and test Agents. All Agents constructed by the Preset share a network model and parameters. However, other objects, such as ReplayBuffers, are independently created for each Agent. The Preset can be saved and loaded from disk.

abstract agent(logger=None, train_steps=inf)

Instantiate a training-mode Agent with the existing model.

Parameters:

• logger (all.logging.Logger, optional) – Coefficient for the entropy term in the total loss.

• train_steps (int, optional) – The number of steps for which the agent will be trained.

Returns:

The instantiated Agent.

Return type:

all.agents.Agent

save(filename)

Save the preset and the contained model to disk.

The preset can later be loaded using torch.load(filename), allowing a test mode agent to be instantiated for evaluation or other purposes.

Parameters:

filename (str) – The path where the preset should be saved.

abstract test_agent()

Instansiate a test-mode Agent with the existing model.

Returns:

The instantiated test Agent.

Return type:

all.agents.Agent

class all.presets.PresetBuilder(default_name, default_hyperparameters, constructor, device='cuda', env=None, hyperparameters=None, name=None)

Bases: object

build()

device(device)

env(env)

hyperparameters(**hyperparameters)

name(name)