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learner_gp.py
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learner_gp.py
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import wandb
import torch.jit
from torch.nn import Linear, Sequential, LeakyReLU
from redis import Redis
from rocket_learn.agent.actor_critic_agent import ActorCriticAgent
from agent import Opti
from rocket_learn.agent.discrete_policy import DiscretePolicy
from rocket_learn.ppo import PPO
from rocket_learn.rollout_generator.redis.redis_rollout_generator import RedisRolloutGenerator
from CoyoteObs import CoyoteObsBuilder
from CoyoteParser import CoyoteAction
import numpy as np
from rewards import ZeroSumReward
import Constants_gp
from utils.misc import count_parameters
import os
from torch import set_num_threads
from rocket_learn.utils.stat_trackers.common_trackers import Speed, Demos, TimeoutRate, Touch, EpisodeLength, Boost, \
BehindBall, TouchHeight, DistToBall, AirTouch, AirTouchHeight, BallHeight, BallSpeed, CarOnGround, GoalSpeed, \
MaxGoalSpeed
# ideas for models:
# get to ball as fast as possible, sometimes with no boost, rewards exist
# pinches (ceiling and kuxir and team?), score in as few touches as possible with high velocity
# half flip, wavedash, wall dash, how to do this one?
# lix reset?
# normal play as well as possible, rewards exist
# aerial play without pinch, rewards exist
# kickoff, 5 second terminal, reward ball distance into opp half
set_num_threads(1)
if __name__ == "__main__":
frame_skip = Constants_gp.FRAME_SKIP
half_life_seconds = Constants_gp.TIME_HORIZON
fps = 120 / frame_skip
gamma = np.exp(np.log(0.5) / (fps * half_life_seconds))
config = dict(
actor_lr=0,
critic_lr=5e-5,
n_steps=Constants_gp.STEP_SIZE,
batch_size=100_000,
minibatch_size=50_000,
epochs=30,
gamma=gamma,
save_every=10,
model_every=100,
ent_coef=0.01,
)
run_id = "gp_run_2v2_3.57"
wandb.login(key=os.environ["WANDB_KEY"])
logger = wandb.init(dir="./wandb_store",
name="GP_Run_2v2_3.57",
project="Opti",
entity="kaiyotech",
id=run_id,
config=config,
settings=wandb.Settings(_disable_stats=True, _disable_meta=True),
resume=True,
)
redis = Redis(username="user1", password=os.environ["redis_user1_key"],
db=Constants_gp.DB_NUM) # host="192.168.0.201",
redis.delete("worker-ids")
stat_trackers = [
Speed(normalize=True), Demos(), TimeoutRate(), Touch(), EpisodeLength(), Boost(), BehindBall(), TouchHeight(),
DistToBall(), AirTouch(), AirTouchHeight(), BallHeight(), BallSpeed(normalize=True), CarOnGround(),
GoalSpeed(), MaxGoalSpeed(),
]
rollout_gen = RedisRolloutGenerator("Opti_GP",
redis,
lambda: CoyoteObsBuilder(expanding=True, tick_skip=Constants_gp.FRAME_SKIP,
team_size=3, extra_boost_info=True,
embed_players=True),
lambda: ZeroSumReward(zero_sum=Constants_gp.ZERO_SUM,
goal_w=10,
concede_w=-10,
# double_tap_w=5,
velocity_bg_w=0.075 / 2, # fix for the tick skip change
velocity_pb_w=0,
boost_gain_w=0,
punish_boost=True,
use_boost_punish_formula=False,
boost_spend_w=-0.45,
boost_gain_small_w=0,
punish_low_boost_w=-0.02,
demo_w=5,
acel_ball_w=1,
team_spirit=1,
# cons_air_touches_w=2,
jump_touch_w=4,
wall_touch_w=4,
touch_grass_w=0,
punish_bad_spacing_w=-0.1,
handbrake_ctrl_w=0,
tick_skip=Constants_gp.FRAME_SKIP,
flatten_wall_height=True,
slow_w=-0.1,
turtle_w=-0.2
),
lambda: CoyoteAction(),
save_every=logger.config.save_every * 3,
model_every=logger.config.model_every,
logger=logger,
clear=False,
stat_trackers=stat_trackers,
# gamemodes=("1v1", "2v2", "3v3"),
max_age=1,
pretrained_agents=Constants_gp.pretrained_agents,
)
critic = Sequential(Linear(426, 512), LeakyReLU(), Linear(512, 512), LeakyReLU(),
Linear(512, 512), LeakyReLU(), Linear(512, 512), LeakyReLU(),
Linear(512, 512), LeakyReLU(), Linear(512, 512), LeakyReLU(),
Linear(512, 512), LeakyReLU(), Linear(512, 512), LeakyReLU(),
Linear(512, 1))
actor = Sequential(Linear(426, 512), LeakyReLU(), Linear(512, 512), LeakyReLU(), Linear(512, 512),
LeakyReLU(),
Linear(512, 512), LeakyReLU(), Linear(512, 512), LeakyReLU(),
Linear(512, 373))
critic = Opti(embedder=Sequential(Linear(35, 128), LeakyReLU(), Linear(128, 35 * 5)), net=critic)
actor = Opti(embedder=Sequential(Linear(35, 128), LeakyReLU(), Linear(128, 35 * 5)), net=actor)
actor = DiscretePolicy(actor, shape=(373,))
optim = torch.optim.Adam([
{"params": actor.parameters(), "lr": logger.config.actor_lr},
{"params": critic.parameters(), "lr": logger.config.critic_lr},
])
agent = ActorCriticAgent(actor=actor, critic=critic, optimizer=optim)
print(f"Gamma is: {gamma}")
count_parameters(agent)
alg = PPO(
rollout_gen,
agent,
ent_coef=logger.config.ent_coef,
n_steps=logger.config.n_steps,
batch_size=logger.config.batch_size,
minibatch_size=logger.config.minibatch_size,
epochs=logger.config.epochs,
gamma=logger.config.gamma,
logger=logger,
zero_grads_with_none=True,
disable_gradient_logging=True,
)
alg.load("GP_saves/Opti_1690394422.0543807/Opti_52080/checkpoint.pt")
alg.agent.optimizer.param_groups[0]["lr"] = logger.config.actor_lr
alg.agent.optimizer.param_groups[1]["lr"] = logger.config.critic_lr
# alg.freeze_policy(100)
alg.run(iterations_per_save=logger.config.save_every, save_dir="GP_saves")