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| Original file line number | Diff line number | Diff line change |
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| # MIT License | ||
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| # Copyright (c) 2023 Replicable-MARL | ||
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| # Permission is hereby granted, free of charge, to any person obtaining a copy | ||
| # of this software and associated documentation files (the "Software"), to deal | ||
| # in the Software without restriction, including without limitation the rights | ||
| # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
| # copies of the Software, and to permit persons to whom the Software is | ||
| # furnished to do so, subject to the following conditions: | ||
| # | ||
| # The above copyright notice and this permission notice shall be included in all | ||
| # copies or substantial portions of the Software. | ||
| # | ||
| # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
| # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
| # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
| # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
| # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
| # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
| # SOFTWARE. | ||
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| env: gobigger | ||
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| env_args: | ||
| map_name: "st_t1p2" # st(andard)_t(eam)2p(layer)2 | ||
| #num_teams: 1 | ||
| #num_agents: 2 | ||
| frame_limit: 1600 | ||
| mask_flag: False | ||
| global_state_flag: False | ||
| opp_action_in_cc: True | ||
| fixed_batch_timesteps: 3200 # optional, all scenario will use this batch size, only valid for on-policy algorithms |
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| Original file line number | Diff line number | Diff line change |
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| # MIT License | ||
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| # Copyright (c) 2023 Replicable-MARL | ||
|
|
||
| # Permission is hereby granted, free of charge, to any person obtaining a copy | ||
| # of this software and associated documentation files (the "Software"), to deal | ||
| # in the Software without restriction, including without limitation the rights | ||
| # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
| # copies of the Software, and to permit persons to whom the Software is | ||
| # furnished to do so, subject to the following conditions: | ||
| # | ||
| # The above copyright notice and this permission notice shall be included in all | ||
| # copies or substantial portions of the Software. | ||
| # | ||
| # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
| # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
| # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
| # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
| # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
| # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
| # SOFTWARE. | ||
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| import copy | ||
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| from gobigger.envs import create_env_custom | ||
| from gym.spaces import Dict as GymDict, Box | ||
| from ray.rllib.env.multi_agent_env import MultiAgentEnv | ||
| import numpy as np | ||
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| policy_mapping_dict = { | ||
| "all_scenario": { | ||
| "description": "mixed scenarios to t>2 (num_teams > 1)", | ||
| "team_prefix": ("team0_", "team1_"), | ||
| "all_agents_one_policy": True, | ||
| "one_agent_one_policy": True, | ||
| }, | ||
| } | ||
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| class RLlibGoBigger(MultiAgentEnv): | ||
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| def __init__(self, env_config): | ||
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| map_name = env_config["map_name"] | ||
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| env_config.pop("map_name", None) | ||
| self.num_agents_per_team = int(map_name.split("p")[-1][0]) | ||
| self.num_teams = int(map_name.split("_t")[1][0]) | ||
| if self.num_teams == 1: | ||
| policy_mapping_dict["all_scenario"]["team_prefix"] = ("team0_",) | ||
| self.num_agents = self.num_agents_per_team * self.num_teams | ||
| self.max_steps = env_config["frame_limit"] | ||
| self.env = create_env_custom(type='st', cfg=dict( | ||
| team_num=self.num_teams, | ||
| player_num_per_team=self.num_agents_per_team, | ||
| frame_limit=self.max_steps | ||
| )) | ||
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| self.action_space = Box(low=-1, | ||
| high=1, | ||
| shape=(2,), | ||
| dtype=float) | ||
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| self.rectangle_dim = 4 | ||
| self.food_dim = self.num_agents * 100 | ||
| self.thorns_dim = self.num_agents * 6 | ||
| self.clone_dim = self.num_agents * 10 | ||
| self.team_name_dim = 1 | ||
| self.score_dim = 1 | ||
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| self.obs_dim = self.rectangle_dim + self.food_dim + self.thorns_dim + \ | ||
| self.clone_dim + self.team_name_dim + self.score_dim | ||
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| self.observation_space = GymDict({"obs": Box( | ||
| low=-1e6, | ||
| high=1e6, | ||
| shape=(self.obs_dim,), | ||
| dtype=float)}) | ||
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| self.agents = [] | ||
| for team_index in range(self.num_teams): | ||
| for agent_index in range(self.num_agents_per_team): | ||
| self.agents.append("team{}_{}".format(team_index, agent_index)) | ||
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| env_config["map_name"] = map_name | ||
| self.env_config = env_config | ||
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| def reset(self): | ||
| original_obs = self.env.reset() | ||
| obs = {} | ||
| for agent_index, agent_name in enumerate(self.agents): | ||
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| rectangle = list(original_obs[1][agent_index]["rectangle"]) | ||
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| overlap_dict = original_obs[1][agent_index]["overlap"] | ||
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| food = overlap_dict["food"] | ||
| if 4 * len(food) > self.food_dim: | ||
| food = food[:self.food_dim // 4] | ||
| else: | ||
| padding = [0] * (self.food_dim - 4 * len(food)) | ||
| food.append(padding) | ||
| food = [item for sublist in food for item in sublist] | ||
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| thorns = overlap_dict["thorns"] | ||
| if 6 * len(thorns) > self.thorns_dim: | ||
| thorns = thorns[:self.thorns_dim // 6] | ||
| else: | ||
| padding = [0] * (self.thorns_dim - 6 * len(thorns)) | ||
| thorns.append(padding) | ||
| thorns = [item for sublist in thorns for item in sublist] | ||
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| clone = overlap_dict["clone"] | ||
| if 10 * len(clone) > self.clone_dim: | ||
| clone = clone[:self.clone_dim // 10] | ||
| else: | ||
| padding = [0] * (self.clone_dim - 10 * len(clone)) | ||
| clone.append(padding) | ||
| clone = [item for sublist in clone for item in sublist] | ||
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| team = original_obs[1][agent_index]["team_name"] | ||
| score = original_obs[1][agent_index]["score"] | ||
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| all_elements = rectangle + food + thorns + clone + [team] + [score] | ||
| all_elements = np.array(all_elements, dtype=float) | ||
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| obs[agent_name] = { | ||
| "obs": all_elements | ||
| } | ||
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| return obs | ||
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| def step(self, action_dict): | ||
| actions = {} | ||
| for i, agent_name in enumerate(self.agents): | ||
| actions[i] = list(action_dict[agent_name]) | ||
| actions[i].append(-1) | ||
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| original_obs, team_rewards, done, info = self.env.step(actions) | ||
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| rewards = {} | ||
| obs = {} | ||
| infos = {} | ||
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| for agent_index, agent_name in enumerate(self.agents): | ||
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| rectangle = list(original_obs[1][agent_index]["rectangle"]) | ||
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| overlap_dict = original_obs[1][agent_index]["overlap"] | ||
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| food = overlap_dict["food"] | ||
| if 4 * len(food) > self.food_dim: | ||
| food = food[:self.food_dim // 4] | ||
| else: | ||
| padding = [0] * (self.food_dim - 4 * len(food)) | ||
| food.append(padding) | ||
| food = [item for sublist in food for item in sublist] | ||
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| thorns = overlap_dict["thorns"] | ||
| if 6 * len(thorns) > self.thorns_dim: | ||
| thorns = thorns[:self.thorns_dim // 6] | ||
| else: | ||
| padding = [0] * (self.thorns_dim - 6 * len(thorns)) | ||
| thorns.append(padding) | ||
| thorns = [item for sublist in thorns for item in sublist] | ||
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| clone = overlap_dict["clone"] | ||
| if 10 * len(clone) > self.clone_dim: | ||
| clone = clone[:self.clone_dim // 10] | ||
| else: | ||
| padding = [0] * (self.clone_dim - 10 * len(clone)) | ||
| clone.append(padding) | ||
| clone = [item for sublist in clone for item in sublist] | ||
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| team = original_obs[1][agent_index]["team_name"] | ||
| score = original_obs[1][agent_index]["score"] | ||
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| all_elements = rectangle + food + thorns + clone + [team] + [score] | ||
| all_elements = np.array(all_elements, dtype=float) | ||
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| obs[agent_name] = { | ||
| "obs": all_elements | ||
| } | ||
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| rewards[agent_name] = team_rewards[team] | ||
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| dones = {"__all__": done} | ||
| return obs, rewards, dones, infos | ||
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| def get_env_info(self): | ||
| env_info = { | ||
| "space_obs": self.observation_space, | ||
| "space_act": self.action_space, | ||
| "num_agents": self.num_agents, | ||
| "episode_limit": self.max_steps, | ||
| "policy_mapping_info": policy_mapping_dict | ||
| } | ||
| return env_info | ||
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| def close(self): | ||
| self.env.close() |
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