mirror of
https://github.com/jcreek/Sarpy.git
synced 2026-07-12 18:53:44 +00:00
Josh Creek
96 lines
4.0 KiB
Python
96 lines
4.0 KiB
Python
from rlbot.agents.base_agent import BaseAgent, SimpleControllerState
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from rlbot.utils.structures.game_data_struct import GameTickPacket
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import numpy as np
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from action.default_act import DefaultAction
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from agent import Agent
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from obs.default_obs import DefaultObs
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from rlgym_compat import GameState
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class RLGymExampleBot(BaseAgent):
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def __init__(self, name, team, index):
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super().__init__(name, team, index)
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# FIXME Hey, botmaker. Start here:
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# Swap the obs builder if you are using a different one, RLGym's AdvancedObs is also available
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self.obs_builder = DefaultObs()
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# Swap the action parser if you are using a different one, RLGym's Discrete and Continuous are also available
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self.act_parser = DefaultAction()
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# Your neural network logic goes inside the Agent class, go take a look inside src/agent.py
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self.agent = Agent()
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# Adjust the tickskip if your agent was trained with a different value
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self.tick_skip = 8
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self.game_state: GameState = None
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self.controls = None
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self.action = None
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self.update_action = True
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self.ticks = 0
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self.prev_time = 0
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print('RLGymExampleBot Ready - Index:', index)
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def initialize_agent(self):
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# Initialize the rlgym GameState object now that the game is active and the info is available
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self.game_state = GameState(self.get_field_info())
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self.ticks = self.tick_skip # So we take an action the first tick
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self.prev_time = 0
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self.controls = SimpleControllerState()
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self.action = np.zeros(8)
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self.update_action = True
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def get_output(self, packet: GameTickPacket) -> SimpleControllerState:
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cur_time = packet.game_info.seconds_elapsed
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delta = cur_time - self.prev_time
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self.prev_time = cur_time
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ticks_elapsed = round(delta * 120)
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self.ticks += ticks_elapsed
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self.game_state.decode(packet, ticks_elapsed)
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if self.update_action:
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self.update_action = False
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# FIXME Hey, botmaker. Verify that this is what you need for your agent
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# By default we treat every match as a 1v1 against a fixed opponent,
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# by doing this your bot can participate in 2v2 or 3v3 matches. Feel free to change this
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player = self.game_state.players[self.index]
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teammates = [p for p in self.game_state.players if p.team_num == self.team]
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opponents = [p for p in self.game_state.players if p.team_num != self.team]
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if len(opponents) == 0:
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# There's no opponent, we assume this model is 1v0
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self.game_state.players = [player]
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else:
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# Sort by distance to ball
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teammates.sort(key=lambda p: np.linalg.norm(self.game_state.ball.position - p.car_data.position))
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opponents.sort(key=lambda p: np.linalg.norm(self.game_state.ball.position - p.car_data.position))
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# Grab opponent in same "position" relative to it's teammates
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opponent = opponents[min(teammates.index(player), len(opponents) - 1)]
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self.game_state.players = [player, opponent]
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obs = self.obs_builder.build_obs(player, self.game_state, self.action)
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self.action = self.act_parser.parse_actions(self.agent.act(obs), self.game_state)[0] # Dim is (N, 8)
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if self.ticks >= self.tick_skip - 1:
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self.update_controls(self.action)
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if self.ticks >= self.tick_skip:
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self.ticks = 0
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self.update_action = True
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return self.controls
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def update_controls(self, action):
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self.controls.throttle = action[0]
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self.controls.steer = action[1]
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self.controls.pitch = action[2]
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self.controls.yaw = 0 if action[5] > 0 else action[3]
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self.controls.roll = action[4]
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self.controls.jump = action[5] > 0
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self.controls.boost = action[6] > 0
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self.controls.handbrake = action[7] > 0
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