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万博1manbetxEnvironments

Model reinforcement learning environment dynamics using Simulink®models

In a reinforcement learning scenario, the environment models the dynamics with which the agent interacts. The environment:

  1. Receives actions from the agent

  2. Outputs observations resulting from the dynamic behavior of the environment model

  3. Generates a reward measuring how well the action contributes to achieving the task

You can create predefined and custom environments using Simulink models. For more information, seeCreate Simulink Reinforcement Learning Environments.

Functions

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rlPredefinedEnv Create a predefined reinforcement learning environment
rlSimulinkEnv Create reinforcement learning environment using dynamic model implemented in万博1manbetx
createIntegratedEnv Create万博1manbetxmodel for reinforcement learning, using reference model as environment
validateEnvironment Validate custom reinforcement learning environment
万博1manbetxsimulinkenvwithagent Reinforcement learning environment with a dynamic model implemented in万博1manbetx
generateRewardFunction Generate a reward function from control specifications to train a reinforcement learning agent
exteriorPenalty 关于外部惩罚值点a bounded region
hyperbolicPenalty Hyperbolic penalty value for a point with respect to a bounded region
barrierPenalty Logarithmic barrier penalty value for a point with respect to a bounded region
rlFiniteSetSpec Create discrete action or observation data specifications for reinforcement learning environments
rlNumericSpec Create continuous action or observation data specifications for reinforcement learning environments
getActionInfo Obtain action data specifications from reinforcement learning environment or agent
getObservationInfo Obtain observation data specifications from reinforcement learning environment or agent
bus2RLSpec Create reinforcement learning data specifications for elements of a万博1manbetxbus
reset Reset environment, agent, experience buffer, or policy object

Blocks

RL Agent Reinforcement learning agent

Topics