use dynamic dispatch for actor selection

src/cli.rs

@@ -70,7 +70,8 @@ pub struct Train {
 arg_enum! {
     #[derive(Debug)]
     pub enum Agent {
-        QLearning
+        QLearning,
+        ApproximateQLearning,
     }
 }
 
@@ -87,6 +88,9 @@ pub fn init_verbosity(opts: &Opts) -> Result<(), Box<dyn std::error::Error>> {
     Ok(())
 }
 
-pub fn get_actor() -> impl Actor {
-    qlearning::QLearningAgent::default()
+pub fn get_actor(agent: Agent) -> Box<dyn Actor> {
+    match agent {
+        Agent::QLearning => Box::new(qlearning::QLearningAgent::default()),
+        Agent::ApproximateQLearning => todo!(),
+    }
 }

src/main.rs

@@ -33,7 +33,7 @@ async fn main() -> Result<(), Box<dyn std::error::Error>> {
     match opts.subcmd {
         SubCommand::Play(sub_opts) => {}
         SubCommand::Train(sub_opts) => {
-            let mut to_train = get_actor();
+            let mut to_train = get_actor(sub_opts.agent);
             to_train.set_learning_rate(sub_opts.learning_rate);
             to_train.set_discount_rate(sub_opts.discount_rate);
             to_train.set_exploration_prob(sub_opts.exploration_prob);
@@ -61,7 +61,7 @@ async fn main() -> Result<(), Box<dyn std::error::Error>> {
     Ok(())
 }
 
-fn train_actor(episodes: usize, mut actor: impl Actor) -> impl Actor {
+fn train_actor(episodes: usize, mut actor: Box<dyn Actor>) -> Box<dyn Actor> {
     let mut rng = rand::rngs::SmallRng::from_entropy();
     let mut avg = 0.0;
 
@@ -109,7 +109,7 @@ fn train_actor(episodes: usize, mut actor: impl Actor) -> impl Actor {
     actor
 }
 
-async fn play_game(mut actor: Option<impl Actor>) -> Result<(), Box<dyn std::error::Error>> {
+async fn play_game(mut actor: Option<Box<dyn Actor>>) -> Result<(), Box<dyn std::error::Error>> {
     let mut rng = rand::rngs::SmallRng::from_entropy();
     let sdl_context = sdl2::init()?;
     let video_subsystem = sdl_context.video()?;