OpenAI-Gym-CartPole-v1-HillClimbing

Implement hill-climbing method in policy based methods with adaptive noise scaling

Gym Environment A pole is attached by an un-actuated joint to a cart, which moves along a frictionless track. The system is controlled by applying a force of +1 or -1 to the cart. The pendulum starts upright, and the goal is to prevent it from falling over. A reward of +1 is provided for every timestep that the pole remains upright. The episode ends when the pole is more than 15 degrees from vertical, or the cart moves more than 2.4 units from the center.

Description

This environment corresponds to the version of the cart-pole problem described by Barto, Sutton, and Anderson in "Neuronlike Adaptive Elements That Can Solve Difficult Learning Control Problem".

A pole is attached by an un-actuated joint to a cart, which moves along a frictionless track. The pendulum is placed upright on the cart and the goal is to balance the pole by applying forces in the left and right direction on the cart.

Action Space

The action is a ndarray with shape (1,) which can take values {0, 1} indicating the direction of the fixed force the cart is pushed with.

| Num | Action                 |
|-----|------------------------|
| 0   | Push cart to the left  |
| 1   | Push cart to the right |

Note: The velocity that is reduced or increased by the applied force is not fixed and it depends on the angle the pole is pointing. The center of gravity of the pole varies the amount of energy needed to move the cart underneath it.

Observation Space

The observation is a ndarray with shape (4,) with the values corresponding to the following positions and velocities:

| Num | Observation           | Min                  | Max                |
|-----|-----------------------|----------------------|--------------------|
| 0   | Cart Position         | -4.8                 | 4.8                |
| 1   | Cart Velocity         | -Inf                 | Inf                |
| 2   | Pole Angle            | ~ -0.418 rad (-24°)  | ~ 0.418 rad (24°)  |
| 3   | Pole Angular Velocity | -Inf                 | Inf                |

Note: While the ranges above denote the possible values for observation space of each element, it is not reflective of the allowed values of the state space in an unterminated episode. Particularly:

• The cart x-position (index 0) can be take values between (-4.8, 4.8), but the episode terminates if the cart leaves the (-2.4, 2.4) range.
• The pole angle can be observed between (-.418, .418) radians (or ±24°), but the episode terminates if the pole angle is not in the range (-.2095, .2095) (or ±12°)

Rewards

Since the goal is to keep the pole upright for as long as possible, a reward of +1 for every step taken, including the termination step, is allotted. The threshold for rewards is 475 for v1.

Starting State

All observations are assigned a uniformly random value in (-0.05, 0.05)

Episode Termination

The episode terminates if any one of the following occurs:

1. Pole Angle is greater than ±12°
2. Cart Position is greater than ±2.4 (center of the cart reaches the edge of the display)
3. Episode length is greater than 500 (200 for v0)

Arguments

gym.make('CartPole-v1')

Instructions

• Follow the instructions in Hill_Climbing.ipynb to train and run the agent!

$ git clone https://github.com/bmaxdk/OpenAI-Gym-MountainCar-v1-HillClimbing.git
$ cd OpenAI-Gym-MountainCar-v0-CrossEntropy

Use jupyter notebook to open Hill_Climbing.ipynb.