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Changed hyperparameters, actions, action selection, and reward system

master
Mark 2023-02-24 14:16:43 -08:00
parent 672d330b62
commit 2ff526a072
Signed by: Mark
GPG Key ID: AD62BB059C2AAEE4
2 changed files with 130 additions and 76 deletions
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116
celeste/celeste_ai/celeste.py
View File

@ -3,6 +3,8 @@ import subprocess
import time
import math
import numpy as np
class CelesteError(Exception):
pass
@ -12,8 +14,12 @@ class CelesteState(NamedTuple):
stage: int
# Player position
# Regular position has 0,0 in top-left,
# centered position has 0,0 in center.
xpos: int
ypos: int
xpos_scaled: float
ypos_scaled: float
# Player velocity
xvel: float
@ -37,28 +43,47 @@ class CelesteState(NamedTuple):
# True if Madeline can dash
can_dash: bool
can_dash_int: int
class Celeste:
action_space = [
"left", # move left
"right", # move right
"jump", # jump
"left", # move left 0
"right", # move right 1
#"jump", # jump
"jump-l", # jump left 2
"jump-r", # jump right 3
"dash-u", # dash up
"dash-r", # dash right
"dash-l", # dash left
"dash-ru", # dash right-up
"dash-lu" # dash left-up
"dash-u", # dash up 4
"dash-r", # dash right 5
"dash-l", # dash left 6
"dash-ru", # dash right-up 7
"dash-lu" # dash left-up 8
]
# Map integers to state values.
# This also determines what data is fed to the model.
state_number_map = [
"xpos",
"ypos",
"next_point_x",
"next_point_y"
#"xpos",
#"ypos",
"xpos_scaled",
"ypos_scaled",
"can_dash_int"
#"next_point_x",
#"next_point_y"
]
# Targets the agent tries to reach.
# The last target MUST be outside the frame.
target_checkpoints = [
[ # Stage 1
#(28, 88), # Start pillar
(60, 80), # Middle pillar
(105, 64), # Right ledge
(25, 40), # Left ledge
(110, 16), # End ledge
(110, -2), # Next stage
]
]
def __init__(
@ -110,19 +135,6 @@ class Celeste:
self._resetting = False # True between a call to .reset() and the first state message from pico.
self._keys = {} # Dictionary of "key": bool
# Targets the agent tries to reach.
# The last target MUST be outside the frame.
self.target_checkpoints = [
[ # Stage 1
#(28, 88), # Start pillar
(60, 80), # Middle pillar
(105, 64), # Right ledge
(25, 40), # Left ledge
(110, 16), # End ledge
(110, -2), # Next stage
]
]
def act(self, action: str):
"""
Specify what keys should be down. This does NOT send key events.
@ -141,6 +153,12 @@ class Celeste:
self._keys["Right"] = True
elif action == "jump":
self._keys["c"] = True
elif action == "jump-l":
self._keys["c"] = True
self._keys["Left"] = True
elif action == "jump-r":
self._keys["c"] = True
self._keys["Right"] = True
elif action == "dash-u":
self._keys["Up"] = True
@ -183,12 +201,12 @@ class Celeste:
[int(self._internal_state["rx"])]
)
if len(self.target_checkpoints) < stage:
if len(Celeste.target_checkpoints) < stage:
next_point_x = None
next_point_y = None
else:
next_point_x = self.target_checkpoints[stage][self._next_checkpoint_idx][0]
next_point_y = self.target_checkpoints[stage][self._next_checkpoint_idx][1]
next_point_x = Celeste.target_checkpoints[stage][self._next_checkpoint_idx][0]
next_point_y = Celeste.target_checkpoints[stage][self._next_checkpoint_idx][1]
return CelesteState(
@ -196,6 +214,8 @@ class Celeste:
xpos = int(self._internal_state["px"]),
ypos = int(self._internal_state["py"]),
xpos_scaled = int(self._internal_state["px"]) / 128.0,
ypos_scaled = int(self._internal_state["py"]) / 128.0,
xvel = float(self._internal_state["vx"]),
yvel = float(self._internal_state["vy"]),
deaths = int(self._internal_state["dc"]),
@ -205,7 +225,8 @@ class Celeste:
next_point_x = next_point_x,
next_point_y = next_point_y,
state_count = self._state_counter,
can_dash = self._internal_state["ds"] == "t"
can_dash = self._internal_state["ds"] == "t",
can_dash_int = 1 if self._internal_state["ds"] == "t" else 0
)
except KeyError:
@ -299,24 +320,36 @@ class Celeste:
self._internal_state[key] = val
# Update checkpoints
tx, ty = self.target_checkpoints[self.state.stage][self._next_checkpoint_idx]
# Calculate distance to each point
x = self.state.xpos
y = self.state.ypos
dist = math.sqrt(
(x-tx)*(x-tx) +
((y-ty)*(y-ty))/2
# Possible modification:
# make x-distance twice as valuable as y-distance
)
dist = np.zeros(len(Celeste.target_checkpoints[self.state.stage]), dtype=np.float16)
for i, c in enumerate(Celeste.target_checkpoints[self.state.stage]):
if i < self._next_checkpoint_idx:
dist[i] = 1000
continue
if dist <= 5:
print(f"Got point {self._next_checkpoint_idx}")
self._next_checkpoint_idx += 1
# Update checkpoints
tx, ty = c
dist[i] = (math.sqrt(
(x-tx)*(x-tx) +
((y-ty)*(y-ty))/2
# Possible modification:
# make x-distance twice as valuable as y-distance
))
min_idx = int(dist.argmin())
dist = int(dist[min_idx])
if dist <= 8:
print(f"Got point {min_idx}")
self._next_checkpoint_idx = min_idx + 1
self._last_checkpoint_state = self._state_counter
# Recalculate distance to new point
tx, ty = self.target_checkpoints[self.state.stage][self._next_checkpoint_idx]
tx, ty = Celeste.target_checkpoints[self.state.stage][self._next_checkpoint_idx]
dist = math.sqrt(
(x-tx)*(x-tx) +
((y-ty)*(y-ty))/2
@ -326,6 +359,7 @@ class Celeste:
elif self._state_counter - self._last_checkpoint_state > self.state_timeout:
self._internal_state["dc"] = str(int(self._internal_state["dc"]) + 1)
self._dist = dist
# Call step callbacks

86
celeste/celeste_ai/train.py
View File

@ -24,6 +24,12 @@ if __name__ == "__main__":
screenshot_dir.mkdir(parents = True, exist_ok = True)
# Remove old screenshots
shots = Path("/home/mark/Desktop").glob("hackcel_*.png")
for s in shots:
s.unlink()
compute_device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu"
)
@ -41,11 +47,15 @@ if __name__ == "__main__":
# EPS_END is the final value of epsilon
# EPS_DECAY controls the rate of exponential decay of epsilon, higher means a slower decay
EPS_START = 0.9
EPS_END = 0.05
EPS_DECAY = 4000
EPS_END = 0.02
EPS_DECAY = 100
# How many times we've reached each point.
# Used to compute epsilon-greedy probability with
# the parameters above.
point_counter = [0] * len(Celeste.target_checkpoints[0])
BATCH_SIZE = 1_000
BATCH_SIZE = 100
# Learning rate of target_net.
# Controls how soft our soft update is.
#
@ -58,7 +68,7 @@ if __name__ == "__main__":
#
# A value of zero makes target_net
# not change at all.
TAU = 0.005
TAU = 0.05
# GAMMA is the discount factor as mentioned in the previous section
@ -90,9 +100,10 @@ if __name__ == "__main__":
target_net.load_state_dict(policy_net.state_dict())
learning_rate = 0.001
optimizer = torch.optim.AdamW(
policy_net.parameters(),
lr = 0.01, # Hyperparameter: learning rate
lr = learning_rate,
amsgrad = True
)
@ -109,6 +120,7 @@ if __name__ == "__main__":
memory = checkpoint["memory"]
episode_number = checkpoint["episode_number"] + 1
steps_done = checkpoint["steps_done"]
point_counter = checkpoint["point_counter"]
def select_action(state, steps_done):
"""
@ -144,7 +156,6 @@ def select_action(state, steps_done):
def optimize_model():
if len(memory) < BATCH_SIZE:
raise Exception(f"Not enough elements in memory for a batch of {BATCH_SIZE}")
@ -189,19 +200,8 @@ def optimize_model():
# out[i, j] = a[ i ][ b[i,j] ]
#
# a is "input," b is "index"
# If this doesn't make sense, RTFD.
# Compute Q(s_t, a).
# - Use policy_net to compute Q(s_t) for each state in the batch.
# This gives a tensor of [ Q(state, left), Q(state, right) ]
#
# - Action batch is a tensor that looks like [ [0], [1], [1], ... ]
# listing the action that was taken in each transition.
# 0 => we went left, 1 => we went right.
#
# This aligns nicely with the output of policy_net. We use
# action_batch to index the output of policy_net's prediction.
#
# This gives us a tensor that contains the return we expect to get
# at that state if we follow the model's advice.
@ -214,8 +214,7 @@ def optimize_model():
# = the maximum reward over all possible actions at state s_t+1.
next_state_values = torch.zeros(BATCH_SIZE, device = compute_device)
# Don't compute gradient for operations in this block.
# If you don't understand what this means, RTFD.
with torch.no_grad():
# Note the use of non_final_mask here.
@ -291,6 +290,15 @@ def on_state_before(celeste):
device = compute_device
).unsqueeze(0)
action = select_action(
pt_state,
point_counter[state.next_point]
)
str_action = Celeste.action_space[action]
"""
action = None
while (action) is None or ((not state.can_dash) and (str_action not in ["left", "right"])):
action = select_action(
@ -298,6 +306,8 @@ def on_state_before(celeste):
steps_done
)
str_action = Celeste.action_space[action]
"""
steps_done += 1
@ -343,37 +353,37 @@ def on_state_after(celeste, before_out):
).unsqueeze(0)
if state.next_point == next_state.next_point:
reward = state.dist - next_state.dist
# Clip rewards that are too large
if reward > 1:
reward = 1
else:
reward = 0
reward = 0
else:
# Reward for reaching a point
reward = 1
reward = next_state.next_point - state.next_point
# Add to point counter
for i in range(state.next_point, state.next_point + reward):
point_counter[i] += 1
reward = reward * 10
pt_reward = torch.tensor([reward], device = compute_device)
# Add this state transition to memory.
memory.append(
Transition(
pt_state, # last state
pt_state,
pt_action,
pt_next_state, # next state
pt_next_state,
pt_reward
)
)
print("==> ", int(reward))
print("==> ", reward)
print("")
loss = None
# Only train the network if we have enough
# transitions in memory to do so.
if len(memory) >= BATCH_SIZE:
@ -407,8 +417,18 @@ def on_state_after(celeste, before_out):
"target_state_dict": target_net.state_dict(),
"optimizer_state_dict": optimizer.state_dict(),
"memory": memory,
"point_counter": point_counter,
"episode_number": episode_number,
"steps_done": steps_done
"steps_done": steps_done,
# Hyperparameters
"eps_start": EPS_START,
"eps_end": EPS_END,
"eps_decay": EPS_DECAY,
"batch_size": BATCH_SIZE,
"tau": TAU,
"learning_rate": learning_rate,
"gamma": GAMMA
}, model_save_path)
@ -421,7 +441,7 @@ def on_state_after(celeste, before_out):
for s in shots:
s.rename(target / s.name)
# Save a prediction graph
# Save a snapshot
if episode_number % archive_interval == 0:
torch.save({
"policy_state_dict": policy_net.state_dict(),