train.py

import gym
import numpy as np
import utils
import matplotlib.pyplot as plt


def sample(theta, env, N):
    """ samples N trajectories using the current policy

    :param theta: the model parameters (shape d x 1)
    :param env: the environment used to sample from
    :param N: number of trajectories to sample
    :return:
        trajectories_gradients: lists with sublists for the gradients for each trajectory rollout
        trajectories_rewards:  lists with sublists for the rewards for each trajectory rollout

    Note: the maximum trajectory length is 200 steps
    """
    total_rewards = []
    total_grads = []

    # TODO

    return total_grads, total_rewards


def train(N, T, delta):
    """

    :param N: number of trajectories to sample in each time step
    :param T: number of iterations to train the model
    :param delta: trust region size
    :return:
        theta: the trained model parameters
        avg_episodes_rewards: list of average rewards for each time step
    """
    theta = np.random.rand(100,1)
    env = gym.make('CartPole-v0')
    env.seed(12345)

    episode_rewards = []

    # TODO

    return theta, episode_rewards

if __name__ == '__main__':
    np.random.seed(1234)
    theta, episode_rewards = train(N=100, T=20, delta=1e-2)
    plt.plot(episode_rewards)
    plt.title("avg rewards per timestep")
    plt.xlabel("timestep")
    plt.ylabel("avg rewards")
    plt.show()