mirror of
https://github.com/jcreek/Sarpy.git
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Josh Creek
103 lines
4.0 KiB
Python
103 lines
4.0 KiB
Python
import numpy as np
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from rlgym_sim.utils.gamestates import GameState
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from rlgym_ppo.util import MetricsLogger
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class ExampleLogger(MetricsLogger):
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def _collect_metrics(self, game_state: GameState) -> list:
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return [game_state.players[0].car_data.linear_velocity,
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game_state.players[0].car_data.rotation_mtx(),
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game_state.orange_score]
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def _report_metrics(self, collected_metrics, wandb_run, cumulative_timesteps):
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avg_linvel = np.zeros(3)
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for metric_array in collected_metrics:
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p0_linear_velocity = metric_array[0]
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avg_linvel += p0_linear_velocity
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avg_linvel /= len(collected_metrics)
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report = {"x_vel":avg_linvel[0],
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"y_vel":avg_linvel[1],
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"z_vel":avg_linvel[2],
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"Cumulative Timesteps":cumulative_timesteps}
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wandb_run.log(report)
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def build_rocketsim_env():
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import rlgym_sim
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from rlgym_sim.utils.reward_functions import CombinedReward
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from rlgym_sim.utils.reward_functions.common_rewards import VelocityPlayerToBallReward, VelocityBallToGoalReward, \
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EventReward, SaveBoostReward
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from rlgym_tools.extra_rewards.kickoff_reward import KickoffReward
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from rlgym_sim.utils.obs_builders import DefaultObs
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from rlgym_sim.utils.terminal_conditions.common_conditions import NoTouchTimeoutCondition, GoalScoredCondition
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from rlgym_sim.utils import common_values
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from rlgym_sim.utils.action_parsers import ContinuousAction
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spawn_opponents = True
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team_size = 1
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game_tick_rate = 120
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tick_skip = 8
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timeout_seconds = 10
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timeout_ticks = int(round(timeout_seconds * game_tick_rate / tick_skip))
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action_parser = ContinuousAction()
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terminal_conditions = [NoTouchTimeoutCondition(timeout_ticks), GoalScoredCondition()]
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rewards_to_combine = (VelocityPlayerToBallReward(),
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VelocityBallToGoalReward(),
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EventReward(
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team_goal=1000.0,
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concede=-100.0,
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shot=10.0,
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save=60.0,
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demo=20.0,
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),
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KickoffReward(),
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SaveBoostReward(),)
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reward_weights = (1.0, 1.0, 1.0, 1.0, 1.0)
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reward_fn = CombinedReward(reward_functions=rewards_to_combine,
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reward_weights=reward_weights)
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obs_builder = DefaultObs(
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pos_coef=np.asarray([1 / common_values.SIDE_WALL_X, 1 / common_values.BACK_NET_Y, 1 / common_values.CEILING_Z]),
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ang_coef=1 / np.pi,
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lin_vel_coef=1 / common_values.CAR_MAX_SPEED,
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ang_vel_coef=1 / common_values.CAR_MAX_ANG_VEL)
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env = rlgym_sim.make(tick_skip=tick_skip,
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team_size=team_size,
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spawn_opponents=spawn_opponents,
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terminal_conditions=terminal_conditions,
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reward_fn=reward_fn,
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obs_builder=obs_builder,
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action_parser=action_parser)
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return env
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if __name__ == "__main__":
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from rlgym_ppo import Learner
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metrics_logger = ExampleLogger()
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# 32 processes
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n_proc = 32
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# educated guess - could be slightly higher or lower
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min_inference_size = max(1, int(round(n_proc * 0.9)))
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learner = Learner(build_rocketsim_env,
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n_proc=n_proc,
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min_inference_size=min_inference_size,
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metrics_logger=metrics_logger,
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ppo_batch_size=50000,
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ts_per_iteration=50000,
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exp_buffer_size=150000,
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ppo_minibatch_size=50000,
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ppo_ent_coef=0.001,
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ppo_epochs=1,
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standardize_returns=True,
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standardize_obs=False,
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save_every_ts=100_000,
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timestep_limit=1_000_000_000,
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log_to_wandb=True)
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learner.learn() |