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mpcrl

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  • Examples
  • Module reference
  • Changelog
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  • User guide
  • Examples
  • Module reference
  • Changelog
  • GitHub
  • PyPI

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Callbacks

  • mpcrl.core.callbacks.CallbackMixin
  • mpcrl.core.callbacks.AgentCallbackMixin
  • mpcrl.core.callbacks.LearningAgentCallbackMixin

Learnable parameters

  • mpcrl.LearnableParameter
  • mpcrl.LearnableParametersDict

Scheduling quantities

  • mpcrl.core.schedulers
    • mpcrl.core.schedulers.Chain
    • mpcrl.core.schedulers.ExponentialScheduler
    • mpcrl.core.schedulers.LinearScheduler
    • mpcrl.core.schedulers.LogLinearScheduler
    • mpcrl.core.schedulers.NoScheduling
    • mpcrl.core.schedulers.Scheduler

Exceptions

  • mpcrl.core.errors
    • mpcrl.core.errors.raise_or_warn_on_mpc_failure
    • mpcrl.core.errors.raise_or_warn_on_update_failure
    • mpcrl.core.errors.MpcSolverError
    • mpcrl.core.errors.MpcSolverWarning
    • mpcrl.core.errors.UpdateError
    • mpcrl.core.errors.UpdateWarning

Update strategy

  • mpcrl.UpdateStrategy

Experience replay

  • mpcrl.ExperienceReplay

Exploring

  • mpcrl.core.exploration.ExplorationStrategy
  • mpcrl.core.exploration.NoExploration
  • mpcrl.core.exploration.GreedyExploration
  • mpcrl.core.exploration.EpsilonGreedyExploration
  • mpcrl.core.exploration.OrnsteinUhlenbeckExploration
  • mpcrl.core.exploration.StepWiseExploration

Warmstarting the MPC solvers

  • mpcrl.WarmStartStrategy

Base agents

  • mpcrl.Agent
  • mpcrl.LearningAgent
  • mpcrl.RlLearningAgent

Reinforcement Learning agents

  • mpcrl.LstdDpgAgent
  • mpcrl.LstdQLearningAgent

Other learning agents

  • mpcrl.GlobOptLearningAgent

Base optimizers

  • mpcrl.optim.base_optimizer.BaseOptimizer
  • mpcrl.optim.GradientBasedOptimizer
  • mpcrl.optim.GradientFreeOptimizer

Gradient-based optimizers

  • mpcrl.optim.GradientDescent
  • mpcrl.optim.NewtonMethod
  • mpcrl.optim.Adam
  • mpcrl.optim.RMSprop

Other components

  • mpcrl.wrappers
    • mpcrl.wrappers.agents
      • mpcrl.wrappers.agents.Evaluate
      • mpcrl.wrappers.agents.LearningWrapper
      • mpcrl.wrappers.agents.Log
      • mpcrl.wrappers.agents.RecordUpdates
      • mpcrl.wrappers.agents.Wrapper
    • mpcrl.wrappers.envs
      • mpcrl.wrappers.envs.MonitorEpisodes
      • mpcrl.wrappers.envs.MonitorInfos
  • mpcrl.util
    • mpcrl.util.control
      • mpcrl.util.control.cbf
      • mpcrl.util.control.dcbf
      • mpcrl.util.control.dlqr
      • mpcrl.util.control.iccbf
      • mpcrl.util.control.lqr
      • mpcrl.util.control.rk4
    • mpcrl.util.geometry
      • mpcrl.util.geometry.ConvexPolytopeUniformSampler
    • mpcrl.util.iters
      • mpcrl.util.iters.bool_cycle
    • mpcrl.util.math
      • mpcrl.util.math.cholesky_added_multiple_identity
      • mpcrl.util.math.clip
      • mpcrl.util.math.dual_norm
      • mpcrl.util.math.lie_derivative
      • mpcrl.util.math.monomial_powers
      • mpcrl.util.math.monomials_basis_function
      • mpcrl.util.math.nchoosek
      • mpcrl.util.math.summarize_array
    • mpcrl.util.named
      • mpcrl.util.named.Named
    • mpcrl.util.seeding
      • mpcrl.util.seeding.mk_seed
  • Module reference
  • mpcrl.util
  • mpcrl.util.seeding
  • mpcrl.util.seeding.mk_seed

mpcrl.util.seeding.mk_seed#

mpcrl.util.seeding.mk_seed(rng)[source]#

Generates a random seed compatible with gymnasium.Env.reset.

Parameters:
rngnumpy.random.Generator

RNG generator.

Returns:
seed

A random integer in the range [0, 2**32).

Return type:

int

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