training and simulation -凯发k8网页登录
train and simulate reinforcement learning agents
during training, the agent continuously updates its parameters to learn the optimal policy for a given environment. during simulation, the agent receives observations and a reward from the environment, and returns an action to the environment without updating its parameters.
reinforcement learning toolbox™ provides functions for training agents and validating the training results through simulation. for an introduction to training and simulating agents, see train reinforcement learning agents.
apps
reinforcement learning designer | design, train, and simulate reinforcement learning agents (since r2021a) |
functions
blocks
topics
use the reinforcement learning designer app
- design and train agent using reinforcement learning designer
design and train a dqn agent for a cart-pole system using the reinforcement learning designer app. - specify training options in reinforcement learning designer
interactively specify options for training reinforcement learning agents using the reinforcement learning designer app. - specify simulation options in reinforcement learning designer
interactively specify options for simulating reinforcement learning agents using the reinforcement learning designer app.
training and simulation basics
- train reinforcement learning agents
find the optimal policy by training your agent within a specified environment. - train reinforcement learning agent in basic grid world
train q-learning and sarsa agents to solve a grid world in matlab®. - train reinforcement learning agent in mdp environment
train a reinforcement learning agent in a generic markov decision process environment. - create simulink environment and train agent
train a controller using reinforcement learning with a plant modeled in simulink® as the training environment. - train reinforcement learning agent for simple contextual bandit problem
train q and dqn agents to solve a contextual bandit problem.
training and simulation advanced
- create dqn agent using deep network designer and train using image observations
create a reinforcement learning agent using the deep network designer app from the deep learning toolbox™. - log training data to disk
log a variety of data to disk while training an agent. - train agent or tune environment parameters using parameter sweeping
tune a ddpg agent using hyperparameter sweeping. - train reinforcement learning agent offline to control quanser qube pendulum
train td3 agent offline to control a quanser qube pendulum. - train biped robot to walk using evolution strategy
train td3 agent using evolutionary strategy.
use multiple processes and gpus
- train agents using parallel computing and gpus
accelerate agent training by running simulations in parallel on multiple cores, gpus, clusters or cloud resources. - train ac agent to balance cart-pole system using parallel computing
train a ac agent for a discrete action space environment using asynchronous parallel computing. - train dqn agent for lane keeping assist using parallel computing
train a dqn agent for an automated driving application using parallel computing.
multi-agent training
- train multiple agents to perform collaborative task
train two continuous action space ppo agents to collaboratively move an object. - train multiple agents for area coverage
train three discrete action space ppo agents to explore a grid-world environment in a collaborative-competitive manner. - train multiple agents for path following control
train a dqn and a ddpg agent to collaboratively perform adaptive cruise control and lane keeping assist to follow a path.
develop custom agents and training algorithms
- train reinforcement learning policy using custom training loop
train a reinforcement learning policy using your own custom training loop. - create and train custom pg agent
create a custom pg agent and train it using the built-in train function. - create and train custom lqr agent
create a custom agent that solves an lqr problem and train it using the built-in train function. - custom training loop with simulink action noise
use a custom training loop to train a continuous action space reinforcement learning policy in simulink when action noise is generated within the model.
train model based policy optimization agents
- train mbpo agent to balance cart-pole system
a model-based reinforcement learning agent learns a model of its environment that it can use to generate additional experiences for training. - model-based reinforcement learning using custom training loop
create a model-based reinforcement learning agent using a custom training loop.