import jax
import jax.numpy as jnp
from typing import List, Tuple, Dict, Any
import gym

class ReplayBuffer:
    def __init__(self, capacity: int):
        self.capacity = capacity
        self.buffer = []
        self.position = 0

    def push(self, state, action, reward, next_state, done):
        if len(self.buffer) < self.capacity:
            self.buffer.append(None)
        self.buffer[self.position] = (state, action, reward, next_state, done)
        self.position = (self.position + 1) % self.capacity

    def sample(self, batch_size: int) -> Dict[str, jnp.ndarray]:
        batch = jax.random.choice(jax.random.PRNGKey(0), len(self.buffer), (batch_size,), replace=False)
        states, actions, rewards, next_states, dones = zip(*[self.buffer[i] for i in batch])
        return {
            'observations': jnp.array(states),
            'actions': jnp.array(actions),
            'rewards': jnp.array(rewards),
            'next_observations': jnp.array(next_states),
            'dones': jnp.array(dones)
        }

def create_train_state(rng, model, input_shape):
    # Placeholder function, actual implementation would depend on the model structure
    return None

def select_action(model, state: jnp.ndarray, epsilon: float) -> int:
    # Placeholder function, actual implementation would depend on the model structure
    return 0

def train_rl_agent(model, env, state, num_episodes: int, max_steps: int) -> Dict[str, Any]:
    episode_rewards = []
    for episode in range(num_episodes):
        obs = env.reset()
        episode_reward = 0
        for step in range(max_steps):
            action = model.select_action(state.params, obs)
            next_obs, reward, done, _ = env.step(action)
            model.replay_buffer.push(obs, action, reward, next_obs, done)
            obs = next_obs
            episode_reward += reward
            if done:
                break
        episode_rewards.append(episode_reward)

        # Update the model
        if len(model.replay_buffer.buffer) >= model.batch_size:
            batch = model.replay_buffer.sample(model.batch_size)
            state, loss = model.update(state, batch)

        # Update epsilon for exploration
        model.epsilon = max(model.epsilon_end, model.epsilon * model.epsilon_decay)

    final_reward = episode_rewards[-1] if episode_rewards else 0.0
    return {'final_reward': final_reward, 'episode_rewards': episode_rewards}

class RLEnvironment:
    def __init__(self, env_name: str):
        self.env = gym.make(env_name)
        self.observation_space = self.env.observation_space
        self.action_space = self.env.action_space

    def reset(self):
        return self.env.reset()

    def step(self, action):
        next_state, reward, done, truncated, info = self.env.step(action)
        return next_state, reward, done, info

    def render(self):
        return self.env.render()

    def close(self):
        return self.env.close()
