rsrl 0.8.1

A fast, extensible reinforcement learning framework in Rust
Documentation 
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use crate::{domains::Batch, fa::StateActionUpdate, policies::Policy, Handler};

#[derive(Clone, Debug, Parameterised)]
#[cfg_attr(
    feature = "serde",
    derive(Serialize, Deserialize),
    serde(crate = "serde_crate")
)]
pub struct REINFORCE<P> {
    #[weights]
    pub policy: P,

    pub alpha: f64,
    pub gamma: f64,
}

impl<P> REINFORCE<P> {
    pub fn new(policy: P, alpha: f64, gamma: f64) -> Self {
        REINFORCE {
            policy,

            alpha,
            gamma,
        }
    }
}

impl<'m, S, P> Handler<&'m Batch<S, P::Action>> for REINFORCE<P>
where P: Policy<S> + Handler<StateActionUpdate<&'m S, &'m <P as Policy<S>>::Action>>
{
    type Response = Vec<P::Response>;
    type Error = P::Error;

    fn handle(&mut self, batch: &'m Batch<S, P::Action>) -> Result<Self::Response, Self::Error> {
        let mut ret = 0.0;

        batch.iter().map(|t| {
            ret = t.reward + self.gamma * ret;

            self.policy.handle(StateActionUpdate {
                state: t.from.state(),
                action: &t.action,
                error: self.alpha * ret,
            })
        }).collect()
    }
}