celeste/celeste_ai/celeste.py

@@ -50,6 +50,7 @@ class Celeste:
 	action_space = [
 		"left",		# move left		0
 		"right",	# move right	1
+		#"jump",	# jump
 		"jump-l",	# jump left		2
 		"jump-r",	# jump right	3
 
@@ -85,13 +86,6 @@ class Celeste:
 		]
 	]
 
-
-	# Maps room_x, room_y coordinates to stage number.
-	stage_map = [
-		[0, 1, 2, 3, 4]
-	]
-
-
 	def __init__(
 			self,
 			pico_path,
@@ -200,7 +194,9 @@ class Celeste:
 	def state(self):
 		try:
 			stage = (
-				Celeste.stage_map
+				[
+					[0, 1, 2, 3, 4]
+				]
 				[int(self._internal_state["ry"])]
 				[int(self._internal_state["rx"])]
 			)

celeste/celeste_ai/train.py

@@ -341,23 +341,10 @@ def on_state_after(celeste, before_out):
 		dtype = torch.long
 	)
 
-	finished_stage = False
-	# No reward if dead
 	if next_state.deaths != 0:
 		pt_next_state = None
 		reward = 0
 
-	# Reward for finishing stage
-	elif next_state.stage >= 1:
-		finished_stage = True
-		reward = next_state.next_point - state.next_point
-		reward += 1
-
-		# Add to point counter
-		for i in range(state.next_point, state.next_point + reward):
-			point_counter[i] += 1
-
-	# Regular reward
 	else:
 		pt_next_state = torch.tensor(
 			[getattr(next_state, x) for x in Celeste.state_number_map],
@@ -414,7 +401,7 @@ def on_state_after(celeste, before_out):
 
 	# Move on to the next episode once we reach
 	# a terminal state.
-	if (next_state.deaths != 0 or finished_stage):
+	if (next_state.deaths != 0):
 		s = celeste.state
 		with model_train_log.open("a") as f:
 			f.write(json.dumps({

celeste/ffmpeg.sh

@@ -22,6 +22,8 @@ render_dir () {
 		$OUTPUT_DIR/${1##*/}.mp4
 }
 
+
+
 # Todo: error out if exists
 mkdir -p $OUTPUT_DIR
 
@@ -48,18 +50,17 @@ ffmpeg \
 	-safe 0 \
 	-i video_merge_list \
 	-vf "scale=1024x1024:flags=neighbor" \
-	$SC_ROOT/1x.mp4
+	$OUTPUT_DIR/00-all.mp4
 
 rm video_merge_list
 
 # Make accelerated video
 ffmpeg \
 	-loglevel error -stats -y \
-	-i $SC_ROOT/1x.mp4 \
+	-i $OUTPUT_DIR/00-all.mp4 \
 	-framerate 60 \
 	-filter:v "setpts=0.125*PTS" \
 	$SC_ROOT/8x.mp4
 
 echo "Cleaning up..."
-
 rm -dr $OUTPUT_DIR

polecart/basic/main.py

@@ -200,12 +200,13 @@ for ep in range(num_episodes):
 
 
 
-		state = next_state
-		
 
 		# Only train the network if we have enough
 		# transitions in memory to do so.
 		if len(memory) >= BATCH_SIZE:
+
+			state = next_state
+
 			# Run optimizer
 			optimize.optimize_model(
 				memory,