Trait border::core::base::Agent

pub trait Agent<E: Env>: Policy<E> {
    fn train(&mut self);
    fn eval(&mut self);
    fn is_train(&self) -> bool;
    fn observe(&mut self, step: Step<E>) -> Option<Record>;
    fn push_obs(&self, obs: &E::Obs);
    fn save<T: AsRef<Path>>(&self, path: T) -> Result<(), Box<dyn Error>>;
    fn load<T: AsRef<Path>>(&mut self, path: T) -> Result<(), Box<dyn Error>>;
}

Represents a trainable policy on an environment.

Required methods

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 observe(&mut self, step: Step<E>) -> Option<Record>

Observe a crate::core::base::Step object. The agent is expected to do training its policy based on the observation.

If an optimization step was performed, it returns Some(crate::core::record::Record), otherwise None.

fn push_obs(&self, obs: &E::Obs)

Push observation to the agent. This method is used when resetting the environment.

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

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

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

Load the agent from the given directory.

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Implementors

impl<E, M, O, A> Agent<E> for DQN<E, M, O, A> where
    E: Env,
    M: Model1<Input = Tensor, Output = Tensor> + Clone,
    E::Obs: Into<M::Input>,
    E::Act: From<Tensor>,
    O: TchBuffer<Item = E::Obs, SubBatch = M::Input>,
    A: TchBuffer<Item = E::Act, SubBatch = Tensor>, 

fn train(&mut self)

fn eval(&mut self)

fn is_train(&self) -> bool

fn push_obs(&self, obs: &E::Obs)

fn observe(&mut self, step: Step<E>) -> Option<Record>

Update model parameters.

When the return value is Some(Record), it includes:

• loss_critic: Loss of critic

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

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

impl<E, M, O, A> Agent<E> for PGDiscrete<E, M, O, A> where
    E: Env,
    M: Model1<Input = Tensor, Output = Tensor>,
    E::Obs: Into<M::Input> + Clone,
    E::Act: From<Tensor>,
    O: TchBuffer<Item = E::Obs, SubBatch = M::Input>,
    A: TchBuffer<Item = E::Act, SubBatch = Tensor>, 

fn train(&mut self)

fn eval(&mut self)

fn is_train(&self) -> bool

fn push_obs(&self, obs: &E::Obs)

fn observe(&mut self, step: Step<E>) -> Option<Record>

Update model parameters.

When the return value is Some(Record), it includes:

• loss: Loss for poligy gradient

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

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

impl<E, M, O, A> Agent<E> for PPODiscrete<E, M, O, A> where
    E: Env,
    M: Model1<Input = Tensor, Output = (Tensor, Tensor)>,
    E::Obs: Into<M::Input> + Clone,
    E::Act: From<Tensor>,
    O: TchBuffer<Item = E::Obs, SubBatch = M::Input>,
    A: TchBuffer<Item = E::Act, SubBatch = Tensor>, 

fn train(&mut self)

fn eval(&mut self)

fn is_train(&self) -> bool

fn push_obs(&self, obs: &E::Obs)

fn observe(&mut self, step: Step<E>) -> Option<Record>

Update model parameters.

When the return value is Some(Record), it includes:

• loss_critic: Loss of critic
• loss_actor: Loss of actor

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

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

impl<E, Q, P, O, A> Agent<E> for DDPG<E, Q, P, O, A> where
    E: Env,
    Q: Model2<Input1 = O::SubBatch, Input2 = A::SubBatch, Output = Tensor> + Clone,
    P: Model1<Output = A::SubBatch> + Clone,
    E::Obs: Into<O::SubBatch>,
    E::Act: From<Tensor>,
    O: TchBuffer<Item = E::Obs, SubBatch = P::Input>,
    A: TchBuffer<Item = E::Act, SubBatch = Tensor>, 

fn train(&mut self)

fn eval(&mut self)

fn is_train(&self) -> bool

fn push_obs(&self, obs: &E::Obs)

fn observe(&mut self, step: Step<E>) -> Option<Record>

Update model parameters.

When the return value is Some(Record), it includes:

• loss_critic: Loss of critic
• loss_actor: Loss of actor

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

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

impl<E, Q, P, O, A> Agent<E> for SAC<E, Q, P, O, A> where
    E: Env,
    Q: Model2<Input1 = O::SubBatch, Input2 = A::SubBatch, Output = Tensor> + Clone,
    P: Model1<Input = Tensor, Output = (Tensor, Tensor)> + Clone,
    E::Obs: Into<O::SubBatch>,
    E::Act: From<Tensor>,
    O: TchBuffer<Item = E::Obs, SubBatch = P::Input>,
    A: TchBuffer<Item = E::Act, SubBatch = Tensor>, 

fn train(&mut self)

fn eval(&mut self)

fn is_train(&self) -> bool

fn push_obs(&self, obs: &E::Obs)

fn observe(&mut self, step: Step<E>) -> Option<Record>

Update model parameters.

When the return value is Some(Record), it includes:

• loss_critic: Loss of critic
• loss_actor: Loss of actor

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

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

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