Add Policy Gradient to Reinforcement Learning examples (#1500)

* added policy_gradient, modified main, ddpg and README

* fixed typo in README

* removed unnecessary imports

* small refactor

* Use clap for picking up the subcommand to run.

---------

Co-authored-by: Laurent <laurent.mazare@gmail.com>

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

@@ -8,6 +8,8 @@ use candle_nn::{
 };
 use rand::{distributions::Uniform, thread_rng, Rng};
 
+use super::gym_env::GymEnv;
+
 pub struct OuNoise {
     mu: f64,
     theta: f64,
@@ -449,3 +451,106 @@ impl DDPG<'_> {
         Ok(())
     }
 }
+
+// The impact of the q value of the next state on the current state's q value.
+const GAMMA: f64 = 0.99;
+// The weight for updating the target networks.
+const TAU: f64 = 0.005;
+// The capacity of the replay buffer used for sampling training data.
+const REPLAY_BUFFER_CAPACITY: usize = 100_000;
+// The training batch size for each training iteration.
+const TRAINING_BATCH_SIZE: usize = 100;
+// The total number of episodes.
+const MAX_EPISODES: usize = 100;
+// The maximum length of an episode.
+const EPISODE_LENGTH: usize = 200;
+// The number of training iterations after one episode finishes.
+const TRAINING_ITERATIONS: usize = 200;
+
+// Ornstein-Uhlenbeck process parameters.
+const MU: f64 = 0.0;
+const THETA: f64 = 0.15;
+const SIGMA: f64 = 0.1;
+
+const ACTOR_LEARNING_RATE: f64 = 1e-4;
+const CRITIC_LEARNING_RATE: f64 = 1e-3;
+
+pub fn run() -> Result<()> {
+    let env = GymEnv::new("Pendulum-v1")?;
+    println!("action space: {}", env.action_space());
+    println!("observation space: {:?}", env.observation_space());
+
+    let size_state = env.observation_space().iter().product::<usize>();
+    let size_action = env.action_space();
+
+    let mut agent = DDPG::new(
+        &Device::Cpu,
+        size_state,
+        size_action,
+        true,
+        ACTOR_LEARNING_RATE,
+        CRITIC_LEARNING_RATE,
+        GAMMA,
+        TAU,
+        REPLAY_BUFFER_CAPACITY,
+        OuNoise::new(MU, THETA, SIGMA, size_action)?,
+    )?;
+
+    let mut rng = rand::thread_rng();
+
+    for episode in 0..MAX_EPISODES {
+        // let mut state = env.reset(episode as u64)?;
+        let mut state = env.reset(rng.gen::<u64>())?;
+
+        let mut total_reward = 0.0;
+        for _ in 0..EPISODE_LENGTH {
+            let mut action = 2.0 * agent.actions(&state)?;
+            action = action.clamp(-2.0, 2.0);
+
+            let step = env.step(vec![action])?;
+            total_reward += step.reward;
+
+            agent.remember(
+                &state,
+                &Tensor::new(vec![action], &Device::Cpu)?,
+                &Tensor::new(vec![step.reward as f32], &Device::Cpu)?,
+                &step.state,
+                step.terminated,
+                step.truncated,
+            );
+
+            if step.terminated || step.truncated {
+                break;
+            }
+            state = step.state;
+        }
+
+        println!("episode {episode} with total reward of {total_reward}");
+
+        for _ in 0..TRAINING_ITERATIONS {
+            agent.train(TRAINING_BATCH_SIZE)?;
+        }
+    }
+
+    println!("Testing...");
+    agent.train = false;
+    for episode in 0..10 {
+        // let mut state = env.reset(episode as u64)?;
+        let mut state = env.reset(rng.gen::<u64>())?;
+        let mut total_reward = 0.0;
+        for _ in 0..EPISODE_LENGTH {
+            let mut action = 2.0 * agent.actions(&state)?;
+            action = action.clamp(-2.0, 2.0);
+
+            let step = env.step(vec![action])?;
+            total_reward += step.reward;
+
+            if step.terminated || step.truncated {
+                break;
+            }
+            state = step.state;
+        }
+        println!("episode {episode} with total reward of {total_reward}");
+    }
+    Ok(())
+}