• documentation
main content

agents -凯发k8网页登录

create and configure reinforcement learning agents using common algorithms, such as sarsa, dqn, ddpg, and ppo

a reinforcement learning agent receives observations and a reward from the environment. using its policy, the agent selects an action based on the observations and reward, and returns the action to the environment. during training, the agent continuously updates the policy parameters based on the action, observations, and reward. doing so, allows the agent to learn the optimal policy for the given environment and reward signal.

reinforcement learning toolbox™ software provides reinforcement learning agents that use several common algorithms, such as sarsa, dqn, ddpg, and ppo. you can also implement other agent algorithms by creating your own custom agents.

for more information, see reinforcement learning agents. for more information on defining policy representations, see .

apps

reinforcement learning designerdesign, train, and simulate reinforcement learning agents

blocks

rl agentreinforcement learning agent

functions

q-learning reinforcement learning agent
sarsa reinforcement learning agent
deep q-network (dqn) reinforcement learning agent
rlacagentactor-critic (ac) reinforcement learning agent
policy gradient (pg) reinforcement learning agent
deep deterministic policy gradient (ddpg) reinforcement learning agent
rltd3agenttwin-delayed deep deterministic (td3) policy gradient reinforcement learning agent
soft actor-critic (sac) reinforcement learning agent
proximal policy optimization (ppo) reinforcement learning agent
trust region policy optimization (trpo) reinforcement learning agent
options for q-learning agent
options for sarsa agent
options for dqn agent
options for pg agent
options for ddpg agent
rltd3agentoptionsoptions for td3 agent
rlacagentoptionsoptions for ac agent
options for ppo agent
options for trpo agent
options for sac agent
options for initializing reinforcement learning agents
regularizer options object to train dqn and sac agents
regularizer options object to train ddpg, td3 and sac agents
model-based policy optimization (mbpo) reinforcement learning agent
options for mbpo agent
extract actor from reinforcement learning agent
extract critic from reinforcement learning agent
set actor of reinforcement learning agent
set critic of reinforcement learning agent
obtain action from agent, actor, or policy object given environment observations
replay memory experience buffer
replay memory experience buffer with prioritized sampling
hindsight replay memory experience buffer
hindsight replay memory experience buffer with prioritized sampling
append experiences to replay memory buffer
sample experiences from replay memory buffer
resize replay memory experience buffer
return all experiences in replay memory buffer
validate experiences for replay memory
generate hindsight experiences from hindsight experience replay buffer
getactioninfoobtain action data specifications from reinforcement learning environment, agent, or experience buffer
getobservationinfoobtain observation data specifications from reinforcement learning environment, agent, or experience buffer
resetreset environment, agent, experience buffer, or policy object

topics

agent basics

  • reinforcement learning agents
    you can create an agent using one of several standard reinforcement learning algorithms or define your own custom agent.

  • interactively create or import agents for training using the reinforcement learning designer app.

agent types


  • create q-learning agents for reinforcement learning.

  • create sarsa agents for reinforcement learning.

  • create dqn agents for reinforcement learning.

  • create policy gradient agents for reinforcement learning.

  • create ddpg agents for reinforcement learning.
  • twin-delayed deep deterministic (td3) policy gradient agents
    create td3 agents for reinforcement learning.

  • create actor-critic agents for reinforcement learning.

  • create ppo agents for reinforcement learning.

  • create trpo agents for reinforcement learning.

  • create sac agents for reinforcement learning.

  • a model-based (mbpo) reinforcement learning agent learns a model of its environment that it can use to generate additional experiences for training.

custom agents

网站地图