Struct border_tch_agent::sac::Sac

pub struct Sac<E, Q, P, R>where
    E: Env,
    Q: SubModel2<Output = Tensor>,
    P: SubModel<Output = (Tensor, Tensor)>,
    R: ReplayBufferBase,
    E::Obs: Into<Q::Input1> + Into<P::Input>,
    E::Act: Into<Q::Input2>,
    Q::Input2: From<Tensor>,
    Q::Config: DeserializeOwned + Serialize + OutDim + Debug + PartialEq + Clone,
    P::Config: DeserializeOwned + Serialize + OutDim + Debug + PartialEq + Clone,
    R::Batch: StdBatchBase,
    <R::Batch as StdBatchBase>::ObsBatch: Into<Q::Input1> + Into<P::Input> + Clone,
    <R::Batch as StdBatchBase>::ActBatch: Into<Q::Input2> + Into<Tensor>,{ /* private fields */ }

Soft actor critic (SAC) agent.

Trait Implementations

impl<E, Q, P, R> Agent<E, R> for Sac<E, Q, P, R>where E: Env, Q: SubModel2<Output = Tensor>, P: SubModel<Output = (Tensor, Tensor)>, R: ReplayBufferBase, E::Obs: Into<Q::Input1> + Into<P::Input>, E::Act: Into<Q::Input2> + From<Tensor>, Q::Input2: From<Tensor>, Q::Config: DeserializeOwned + Serialize + OutDim + Debug + PartialEq + Clone, P::Config: DeserializeOwned + Serialize + OutDim + Debug + PartialEq + Clone, R::Batch: StdBatchBase, <R::Batch as StdBatchBase>::ObsBatch: Into<Q::Input1> + Into<P::Input> + Clone, <R::Batch as StdBatchBase>::ActBatch: Into<Q::Input2> + Into<Tensor>,

fn train(&mut self)

Set the policy to training mode.

fn eval(&mut self)

Set the policy to evaluation mode.

fn is_train(&self) -> bool

Return if it is in training mode.

fn opt(&mut self, buffer: &mut R) -> Option<Record>

Do an optimization step.

fn save<T: AsRef<Path>>(&self, path: T) -> Result<()>

Save the agent in the given directory. This method commonly creates a number of files consisting the agent in the directory. For example, the DQN agent in border_tch_agent crate saves two Q-networks corresponding to the original and target networks.

fn load<T: AsRef<Path>>(&mut self, path: T) -> Result<()>

Load the agent from the given directory.

impl<E, Q, P, R> Policy<E> for Sac<E, Q, P, R>where E: Env, Q: SubModel2<Output = Tensor>, P: SubModel<Output = (Tensor, Tensor)>, R: ReplayBufferBase, E::Obs: Into<Q::Input1> + Into<P::Input>, E::Act: Into<Q::Input2> + From<Tensor>, Q::Input2: From<Tensor>, Q::Config: DeserializeOwned + Serialize + OutDim + Debug + PartialEq + Clone, P::Config: DeserializeOwned + Serialize + OutDim + Debug + PartialEq + Clone, R::Batch: StdBatchBase, <R::Batch as StdBatchBase>::ObsBatch: Into<Q::Input1> + Into<P::Input> + Clone, <R::Batch as StdBatchBase>::ActBatch: Into<Q::Input2> + Into<Tensor>,

fn build(config: Self::Config) -> Self

Constructs Sac agent.

type Config = SacConfig<Q, P>

Configuration of the policy.

fn sample(&mut self, obs: &E::Obs) -> E::Act

Sample an action given an observation.