Add some reinforcement learning example. (#1090)

* Add some reinforcement learning example.

* Python initialization.

* Get the example to run.

* Vectorized gym envs for the atari wrappers.

* Get some simulation loop to run.

candle-examples/examples/reinforcement-learning/main.rs

@@ -0,0 +1,75 @@
+#![allow(unused)]
+
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+
+mod gym_env;
+mod vec_gym_env;
+
+use candle::Result;
+use clap::Parser;
+use rand::Rng;
+
+// The total number of episodes.
+const MAX_EPISODES: usize = 100;
+// The maximum length of an episode.
+const EPISODE_LENGTH: usize = 200;
+
+#[derive(Parser, Debug, Clone)]
+#[command(author, version, about, long_about = None)]
+struct Args {
+    /// Run on CPU rather than on GPU.
+    #[arg(long)]
+    cpu: bool,
+
+    /// Enable tracing (generates a trace-timestamp.json file).
+    #[arg(long)]
+    tracing: bool,
+}
+
+fn main() -> Result<()> {
+    use tracing_chrome::ChromeLayerBuilder;
+    use tracing_subscriber::prelude::*;
+
+    let args = Args::parse();
+
+    let _guard = if args.tracing {
+        let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
+        tracing_subscriber::registry().with(chrome_layer).init();
+        Some(guard)
+    } else {
+        None
+    };
+
+    let env = gym_env::GymEnv::new("Pendulum-v1")?;
+    println!("action space: {}", env.action_space());
+    println!("observation space: {:?}", env.observation_space());
+
+    let _num_obs = env.observation_space().iter().product::<usize>();
+    let _num_actions = env.action_space();
+
+    let mut rng = rand::thread_rng();
+
+    for episode in 0..MAX_EPISODES {
+        let mut obs = env.reset(episode as u64)?;
+
+        let mut total_reward = 0.0;
+        for _ in 0..EPISODE_LENGTH {
+            let actions = rng.gen_range(-2.0..2.0);
+
+            let step = env.step(vec![actions])?;
+            total_reward += step.reward;
+
+            if step.is_done {
+                break;
+            }
+            obs = step.obs;
+        }
+
+        println!("episode {episode} with total reward of {total_reward}");
+    }
+    Ok(())
+}