Docs-fix: Pistonball

pettingzoo/butterfly/pistonball/pistonball.py

@@ -24,16 +24,18 @@
 | State Values         | (0, 255)                                             |
 
 
-This is a simple physics based cooperative game where the goal is to move the ball to the left wall of the game border by activating the vertically moving pistons. Each piston agent's observation is an RGB image of the two pistons (or the wall) next to the agent and the space above them. Every
-piston can be acted on in any given time. The action space in discrete mode is 0 to move down, 1 to stay still, and 2 to move up. In continuous mode, the value in the range [-1, 1] is proportional to the amount that the pistons are raised or lowered by. Continuous actions are scaled by a factor
-of 4, so that in both the discrete and continuous action space, the action 1 will move a piston 4 pixels up, and -1 will move pistons 4 pixels down.
+This is a physics based cooperative game where the goal is to move the ball to the left-wall of the game border by activating the vertically moving pistons. To achieve an optimal policy for the environment, pistons must learn highly coordinated behavior.
 
-Accordingly, pistons must learn highly coordinated emergent behavior to achieve an optimal policy for the environment. Each agent gets a reward that is a combination of how much the ball moved left overall and how much the ball moved left if it was close to the piston (i.e. movement the piston
-contributed to). A piston is considered close to the ball if it is directly below any part of the ball. Balancing the ratio between these local and global rewards appears to be critical to learning this environment, and as such is an environment parameter. The local reward applied is 0.5 times
-the change in the ball's x-position. Additionally, the global reward is change in x-position divided by the starting position, times 100, plus the `time_penalty` (default -0.1). For each piston, the reward is `local_ratio` * local_reward + (1-`local_ratio`) * global_reward. The local reward is
-applied to pistons surrounding the ball while the global reward is provided to all pistons.
+**Observations**: Each piston-agent's observation is an RGB image encompassing the piston, its immediate neighbors (either two pistons or a piston and left/right-wall) and the space above them (which may show the ball).
 
-Pistonball uses the chipmunk physics engine, and are thus the physics are about as realistic as in the game Angry Birds.
+**Actions**: Every piston can be acted on at each time step. In discrete mode, the action space is 0 to move down by 4 pixels, 1 to stay still, and 2 to move up by 4 pixels. In continuous mode, the value in the range [-1, 1] is proportional to the amount that the pistons
+are lowered or raised by. Continuous actions are scaled by a factor of 4 to allow for matching the distance travelled in discrete mode, e.g. an action of -1 moves the piston down 4 pixels.
+
+**Rewards**: The same reward is provided to each agent based on how much the ball moved left in the last time-step (moving right results in a negative reward) plus a constant time-penalty. The distance component is the percentage of the initial total distance (i.e. at game-start)
+to the left-wall travelled in the past timestep. For example, if the ball began the game 300 pixels away from the wall, began the time-step 180 pixels away and finished the time-step 175 pixels away, the distance reward would be 100 * 5/300 = 1.7. There is also a configurable
+time-penalty (default: -0.1)  added to the distance-based reward at each time-step. For example, if the ball does not move in a time-step, the reward will be -0.1 not 0. This is to incentivize solving the game faster.
+
+Pistonball uses the chipmunk physics engine, so the physics are about as realistic as in the game Angry Birds.
 
 Keys *a* and *d* control which piston is selected to move (initially the rightmost piston is selected) and keys *w* and *s* move the piston in the vertical direction.
 
@@ -92,8 +94,6 @@
 from pettingzoo.utils import AgentSelector, wrappers
 from pettingzoo.utils.conversions import parallel_wrapper_fn
 
-_image_library = {}
-
 FPS = 20
 
 __all__ = ["ManualPolicy", "env", "parallel_env", "raw_env"]
@@ -236,8 +236,7 @@ def __init__(
         )
         self.recentPistons = set()  # Set of pistons that have touched the ball recently
         self.time_penalty = time_penalty
-        # TODO: this was a bad idea and the logic this uses should be removed at some point
-        self.local_ratio = 0
+
         self.ball_mass = ball_mass
         self.ball_friction = ball_friction
         self.ball_elasticity = ball_elasticity
@@ -463,8 +462,8 @@ def reset(self, seed=None, options=None):
                 -6 * math.pi, 6 * math.pi
             )
 
-        self.lastX = int(self.ball.position[0] - self.ball_radius)
-        self.distance = self.lastX - self.wall_width
+        self.ball_prev_pos = self._get_ball_position()
+        self.distance_to_wall_at_game_start = self.ball_prev_pos - self.wall_width
 
         self.draw_background()
         self.draw()
@@ -563,30 +562,6 @@ def draw(self):
             )
         self.draw_pistons()
 
-    def get_nearby_pistons(self):
-        # first piston = leftmost
-        nearby_pistons = []
-        ball_pos = int(self.ball.position[0] - self.ball_radius)
-        closest = abs(self.pistonList[0].position.x - ball_pos)
-        closest_piston_index = 0
-        for i in range(self.n_pistons):
-            next_distance = abs(self.pistonList[i].position.x - ball_pos)
-            if next_distance < closest:
-                closest = next_distance
-                closest_piston_index = i
-
-        if closest_piston_index > 0:
-            nearby_pistons.append(closest_piston_index - 1)
-        nearby_pistons.append(closest_piston_index)
-        if closest_piston_index < self.n_pistons - 1:
-            nearby_pistons.append(closest_piston_index + 1)
-
-        return nearby_pistons
-
-    def get_local_reward(self, prev_position, curr_position):
-        local_reward = 0.5 * (prev_position - curr_position)
-        return local_reward
-
     def render(self):
         if self.render_mode is None:
             gymnasium.logger.warn(
@@ -610,6 +585,18 @@ def render(self):
             else None
         )
 
+    def _get_ball_position(self) -> int:
+        """Return the leftmost x-position of the ball.
+
+        That leftmost x-position is generally referred to and treated as the 
+        balls' position in this class. If the ball extends beyond the leftmost 
+        wall, return the position of that wall-edge.
+        """
+        ball_position = int(self.ball.position[0] - self.ball_radius)
+        # Check if the ball is touching/within the left-most wall.
+        clipped_ball_position = max(self.wall_width, ball_position)
+        return clipped_ball_position
+
     def step(self, action):
         if (
             self.terminations[self.agent_selection]
@@ -630,30 +617,31 @@ def step(self, action):
 
         self.space.step(self.dt)
         if self._agent_selector.is_last():
-            ball_min_x = int(self.ball.position[0] - self.ball_radius)
-            ball_next_x = (
-                self.ball.position[0]
-                - self.ball_radius
-                + self.ball.velocity[0] * self.dt
-            )
-            if ball_next_x <= self.wall_width + 1:
+            ball_curr_pos = self._get_ball_position()
+
+            # A rough, first-order prediction (i.e. velocity-only) of the balls next position.
+            # The physics environment may bounce the ball off the wall in the next time-step
+            # without us first registering that win-condition.
+            ball_predicted_next_pos = ball_curr_pos + self.ball.velocity[0] * self.dt
+            # Include a single-pixel fudge-factor for the approximation.
+            if ball_predicted_next_pos <= self.wall_width + 1:
                 self.terminate = True
-            # ensures that the ball can't pass through the wall
-            ball_min_x = max(self.wall_width, ball_min_x)
+
             self.draw()
-            local_reward = self.get_local_reward(self.lastX, ball_min_x)
-            # Opposite order due to moving right to left
-            global_reward = (100 / self.distance) * (self.lastX - ball_min_x)
+
+            # The negative one is included since the x-axis increases from left-to-right. And, if the x
+            # position decreases we want the reward to be positive, since the ball would have gotten closer
+            # to the left-wall.
+            reward = (
+                -1
+                * (ball_curr_pos - self.ball_prev_pos)
+                * (100 / self.distance_to_wall_at_game_start)
+            )
             if not self.terminate:
-                global_reward += self.time_penalty
-            total_reward = [
-                global_reward * (1 - self.local_ratio)
-            ] * self.n_pistons  # start with global reward
-            local_pistons_to_reward = self.get_nearby_pistons()
-            for index in local_pistons_to_reward:
-                total_reward[index] += local_reward * self.local_ratio
-            self.rewards = dict(zip(self.agents, total_reward))
-            self.lastX = ball_min_x
+                reward += self.time_penalty
+
+            self.rewards = {agent: reward for agent in self.agents}
+            self.ball_prev_pos = ball_curr_pos
             self.frames += 1
         else:
             self._clear_rewards()