package fehu

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Reinforcement learning framework for OCaml

Install 

dune-project
 Dependency

github.com Readme Changelog Edit opam file Versions (2)

Authors

  1. Thibaut Mattio

Maintainers

  1. Thibaut Mattio

Sources

raven-1.0.0.alpha2.tbz
sha256=93abc49d075a1754442ccf495645bc4fdc83e4c66391ec8aca8fa15d2b4f44d2
sha512=5eb958c51f30ae46abded4c96f48d1825f79c7ce03f975f9a6237cdfed0d62c0b4a0774296694def391573d849d1f869919c49008acffca95946b818ad325f6f

doc/fehu.algorithms/Fehu_algorithms/Reinforce/index.html

Module Fehu_algorithms.ReinforceSource

Reinforce algorithm implementation.

REINFORCE (Monte Carlo Policy Gradient) is a classic policy gradient method that collects complete episodes and updates the policy using Monte Carlo return estimates. See Reinforce for detailed documentation.

Monte Carlo policy gradient (REINFORCE) training API.

Sourcetype config = {
  1. learning_rate : float;
  2. gamma : float;
  3. use_baseline : bool;
  4. reward_scale : float;
  5. entropy_coef : float;
  6. max_episode_steps : int;
}
Sourceval default_config : config
Sourcetype params = Kaun.Ptree.t
Sourcetype metrics = {
  1. episode_return : float;
  2. episode_length : int;
  3. episode_won : bool;
  4. stage_desc : string;
  5. avg_entropy : float;
  6. avg_log_prob : float;
  7. adv_mean : float;
  8. adv_std : float;
  9. value_loss : float option;
  10. total_steps : int;
  11. total_episodes : int;
}
Sourcetype state
Sourceval init : ?baseline_network:Kaun.module_ -> env: ((float, Bigarray.float32_elt) Rune.t, (int32, Bigarray.int32_elt) Rune.t, 'render) Fehu.Env.t -> policy_network:Kaun.module_ -> rng:Rune.Rng.key -> config:config -> unit -> params * state
Sourceval step : env: ((float, Bigarray.float32_elt) Rune.t, (int32, Bigarray.int32_elt) Rune.t, 'render) Fehu.Env.t -> params:params -> state:state -> params * state
Sourceval metrics : state -> metrics
Sourceval train : ?baseline_network:Kaun.module_ -> env: ((float, Bigarray.float32_elt) Rune.t, (int32, Bigarray.int32_elt) Rune.t, 'render) Fehu.Env.t -> policy_network:Kaun.module_ -> rng:Rune.Rng.key -> config:config -> total_timesteps:int -> ?callback:(metrics -> [ `Continue | `Stop ]) -> unit -> params * state
Sourceval save : path:string -> params:params -> state:state -> unit
Sourceval load : path:string -> env: ((float, Bigarray.float32_elt) Rune.t, (int32, Bigarray.int32_elt) Rune.t, 'render) Fehu.Env.t -> policy_network:Kaun.module_ -> ?baseline_network:Kaun.module_ -> config:config -> unit -> (params * state, string) result