Trainers¶

Trainers are responsible for orchestrating and managing the training/evaluation of agents and their interactions with the environment.

Trainers
Sequential trainer	\(\blacksquare\)	\(\blacksquare\)
Parallel trainer	\(\blacksquare\)	\(\square\)
Step trainer	\(\blacksquare\)	\(\blacksquare\)
Manual training	\(\blacksquare\)	\(\blacksquare\)

Base class¶

Note

This is the base class for all the other classes in this module. It provides the basic functionality for the other classes. It is not intended to be used directly.

Basic inheritance usage¶

from typing import Union, List, Optional

import copy

from skrl.envs.wrappers.torch import Wrapper
from skrl.agents.torch import Agent

from skrl.trainers.torch import Trainer


CUSTOM_DEFAULT_CONFIG = {
    "timesteps": 100000,            # number of timesteps to train for
    "headless": False,              # whether to use headless mode (no rendering)
    "disable_progressbar": False,   # whether to disable the progressbar. If None, disable on non-TTY
    "close_environment_at_exit": True,   # whether to close the environment on normal program termination
}


class CustomTrainer(Trainer):
    def __init__(self,
                 env: Wrapper,
                 agents: Union[Agent, List[Agent], List[List[Agent]]],
                 agents_scope: Optional[List[int]] = None,
                 cfg: Optional[dict] = None) -> None:
        """
        :param env: Environment to train on
        :type env: skrl.envs.wrappers.torch.Wrapper
        :param agents: Agents to train
        :type agents: Union[Agent, List[Agent]]
        :param agents_scope: Number of environments for each agent to train on (default: [])
        :type agents_scope: tuple or list of integers
        :param cfg: Configuration dictionary
        :type cfg: dict, optional
        """
        _cfg = copy.deepcopy(CUSTOM_DEFAULT_CONFIG)
        _cfg.update(cfg if cfg is not None else {})
        agents_scope = agents_scope if agents_scope is not None else []
        super().__init__(env=env, agents=agents, agents_scope=agents_scope, cfg=_cfg)

        # ================================
        # - init agents
        # ================================

    def train(self) -> None:
        """Train the agents
        """
        # ================================
        # - run training loop
        #   + call agents.pre_interaction(...)
        #   + compute actions using agents.act(...)
        #   + step environment using env.step(...)
        #   + render scene using env.render(...)
        #   + record environment transition in memory using agents.record_transition(...)
        #   + call agents.post_interaction(...)
        #   + reset environment using env.reset(...)
        # ================================

    def eval(self) -> None:
        """Evaluate the agents
        """
        # ================================
        # - run evaluation loop
        #   + compute actions using agents.act(...)
        #   + step environment using env.step(...)
        #   + render scene using env.render(...)
        #   + call agents.post_interaction(...) parent method to write data to TensorBoard
        #   + reset environment using env.reset(...)
        # ================================

from typing import Union, List, Optional

import copy

from skrl.envs.wrappers.jax import Wrapper
from skrl.agents.jax import Agent

from skrl.trainers.jax import Trainer


CUSTOM_DEFAULT_CONFIG = {
    "timesteps": 100000,            # number of timesteps to train for
    "headless": False,              # whether to use headless mode (no rendering)
    "disable_progressbar": False,   # whether to disable the progressbar. If None, disable on non-TTY
    "close_environment_at_exit": True,   # whether to close the environment on normal program termination
}


class CustomTrainer(Trainer):
    def __init__(self,
                 env: Wrapper,
                 agents: Union[Agent, List[Agent], List[List[Agent]]],
                 agents_scope: Optional[List[int]] = None,
                 cfg: Optional[dict] = None) -> None:
        """
        :param env: Environment to train on
        :type env: skrl.envs.wrappers.jax.Wrapper
        :param agents: Agents to train
        :type agents: Union[Agent, List[Agent]]
        :param agents_scope: Number of environments for each agent to train on (default: [])
        :type agents_scope: tuple or list of integers
        :param cfg: Configuration dictionary
        :type cfg: dict, optional
        """
        _cfg = copy.deepcopy(CUSTOM_DEFAULT_CONFIG)
        _cfg.update(cfg if cfg is not None else {})
        agents_scope = agents_scope if agents_scope is not None else []
        super().__init__(env=env, agents=agents, agents_scope=agents_scope, cfg=_cfg)

        # ================================
        # - init agents
        # ================================

    def train(self) -> None:
        """Train the agents
        """
        # ================================
        # - run training loop
        #   + call agents.pre_interaction(...)
        #   + compute actions using agents.act(...)
        #   + step environment using env.step(...)
        #   + render scene using env.render(...)
        #   + record environment transition in memory using agents.record_transition(...)
        #   + call agents.post_interaction(...)
        #   + reset environment using env.reset(...)
        # ================================

    def eval(self) -> None:
        """Evaluate the agents
        """
        # ================================
        # - run evaluation loop
        #   + compute actions using agents.act(...)
        #   + step environment using env.step(...)
        #   + render scene using env.render(...)
        #   + call agents.post_interaction(...) parent method to write data to TensorBoard
        #   + reset environment using env.reset(...)
        # ================================

API (PyTorch)¶

class skrl.trainers.torch.base.Trainer(env: Wrapper, agents: Agent | List[Agent], agents_scope: List[int] | None = None, cfg: dict | None = None)¶

Bases: object

__init__(env: Wrapper, agents: Agent | List[Agent], agents_scope: List[int] | None = None, cfg: dict | None = None) → None¶

Base class for trainers

Parameters:

env (skrl.envs.wrappers.torch.Wrapper) – Environment to train on
agents (Union[Agent, List[Agent]]) – Agents to train
agents_scope (tuple or list of int, optional) – Number of environments for each agent to train on (default: None)
cfg (dict, optional) – Configuration dictionary (default: None)

__str__() → str¶

Generate a string representation of the trainer

Returns:: Representation of the trainer as string
Return type:: str

_setup_agents() → None¶

Setup agents for training

Raises:: ValueError – Invalid setup

eval() → None¶

Evaluate the agents

Raises:: NotImplementedError – Not implemented

multi_agent_eval() → None¶

Evaluate multi-agents

This method executes the following steps in loop:

Compute actions (sequentially)
Interact with the environments
Render scene
Reset environments

multi_agent_train() → None¶

Train multi-agents

This method executes the following steps in loop:

Pre-interaction
Compute actions
Interact with the environments
Render scene
Record transitions
Post-interaction
Reset environments

single_agent_eval() → None¶

Evaluate agent

This method executes the following steps in loop:

Compute actions (sequentially)
Interact with the environments
Render scene
Reset environments

single_agent_train() → None¶

Train agent

This method executes the following steps in loop:

Pre-interaction
Compute actions
Interact with the environments
Render scene
Record transitions
Post-interaction
Reset environments

train() → None¶

Train the agents

Raises:: NotImplementedError – Not implemented

API (JAX)¶

class skrl.trainers.jax.base.Trainer(env: Wrapper, agents: Agent | List[Agent], agents_scope: List[int] | None = None, cfg: dict | None = None)¶

Bases: object

__init__(env: Wrapper, agents: Agent | List[Agent], agents_scope: List[int] | None = None, cfg: dict | None = None) → None¶

Base class for trainers

Parameters:

env (skrl.envs.wrappers.jax.Wrapper) – Environment to train on
agents (Union[Agent, List[Agent]]) – Agents to train
agents_scope (tuple or list of int, optional) – Number of environments for each agent to train on (default: None)
cfg (dict, optional) – Configuration dictionary (default: None)

__str__() → str¶

Generate a string representation of the trainer

Returns:: Representation of the trainer as string
Return type:: str

_setup_agents() → None¶

Setup agents for training

Raises:: ValueError – Invalid setup

eval() → None¶

Evaluate the agents

Raises:: NotImplementedError – Not implemented

multi_agent_eval() → None¶

Evaluate multi-agents

This method executes the following steps in loop:

Compute actions (sequentially)
Interact with the environments
Render scene
Reset environments

multi_agent_train() → None¶

Train multi-agents

This method executes the following steps in loop:

Pre-interaction
Compute actions
Interact with the environments
Render scene
Record transitions
Post-interaction
Reset environments

single_agent_eval() → None¶

Evaluate agent

This method executes the following steps in loop:

Compute actions (sequentially)
Interact with the environments
Render scene
Reset environments

single_agent_train() → None¶

Train agent

This method executes the following steps in loop:

Pre-interaction
Compute actions
Interact with the environments
Render scene
Record transitions
Post-interaction
Reset environments

train() → None¶

Train the agents

Raises:: NotImplementedError – Not implemented