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add snake environment
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| Original file line number | Diff line number | Diff line change |
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| This is the example for the snake game. | ||
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| ## Usage | ||
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| ```bash | ||
| python train_selfplay.py | ||
| ``` | ||
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| ## Submit to JiDi | ||
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| Submition site: http://www.jidiai.cn/env_detail?envid=1. | ||
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| Snake senarios: [here](https://github.com/jidiai/ai_lib/blob/7a6986f0cb543994277103dbf605e9575d59edd6/env/config.json#L94) | ||
| Original Snake environment: [here](https://github.com/jidiai/ai_lib/blob/master/env/snakes.py) | ||
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| seed: 0 | ||
| callbacks: | ||
| - id: "ProgressBarCallback" |
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| # -*- coding:utf-8 -*- | ||
| def sample_single_dim(action_space_list_each, is_act_continuous): | ||
| if is_act_continuous: | ||
| each = action_space_list_each.sample() | ||
| else: | ||
| if action_space_list_each.__class__.__name__ == "Discrete": | ||
| each = [0] * action_space_list_each.n | ||
| idx = action_space_list_each.sample() | ||
| each[idx] = 1 | ||
| elif action_space_list_each.__class__.__name__ == "MultiDiscreteParticle": | ||
| each = [] | ||
| nvec = action_space_list_each.high - action_space_list_each.low + 1 | ||
| sample_indexes = action_space_list_each.sample() | ||
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| for i in range(len(nvec)): | ||
| dim = nvec[i] | ||
| new_action = [0] * dim | ||
| index = sample_indexes[i] | ||
| new_action[index] = 1 | ||
| each.extend(new_action) | ||
| return each | ||
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| def my_controller(observation, action_space, is_act_continuous): | ||
| joint_action = [] | ||
| for i in range(len(action_space)): | ||
| player = sample_single_dim(action_space[i], is_act_continuous) | ||
| joint_action.append(player) | ||
| return joint_action |
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| #!/usr/bin/env python | ||
| # -*- coding: utf-8 -*- | ||
| # Copyright 2023 The OpenRL Authors. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # https://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
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| """""" | ||
| import time | ||
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| import numpy as np | ||
| from wrappers import ConvertObs | ||
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| from openrl.envs.snake.snake import SnakeEatBeans | ||
| from openrl.envs.snake.snake_pettingzoo import SnakeEatBeansAECEnv | ||
| from openrl.selfplay.wrappers.random_opponent_wrapper import RandomOpponentWrapper | ||
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| def test_raw_env(): | ||
| env = SnakeEatBeans() | ||
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| obs, info = env.reset() | ||
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| done = False | ||
| while not np.any(done): | ||
| a1 = np.zeros(4) | ||
| a1[env.action_space.sample()] = 1 | ||
| a2 = np.zeros(4) | ||
| a2[env.action_space.sample()] = 1 | ||
| obs, reward, done, info = env.step([a1, a2]) | ||
| print("obs:", obs) | ||
| print("reward:", reward) | ||
| print("done:", done) | ||
| print("info:", info) | ||
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| def test_aec_env(): | ||
| from PIL import Image | ||
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| img_list = [] | ||
| env = SnakeEatBeansAECEnv(render_mode="rgb_array") | ||
| env.reset(seed=0) | ||
| # time.sleep(1) | ||
| img = env.render() | ||
| img_list.append(img) | ||
| step = 0 | ||
| for player_name in env.agent_iter(): | ||
| if step > 20: | ||
| break | ||
| observation, reward, termination, truncation, info = env.last() | ||
| if termination or truncation: | ||
| break | ||
| action = env.action_space(player_name).sample() | ||
| # if player_name == "player_0": | ||
| # action = 2 | ||
| # elif player_name == "player_1": | ||
| # action = 3 | ||
| # else: | ||
| # raise ValueError("Unknown player name: {}".format(player_name)) | ||
| env.step(action) | ||
| img = env.render() | ||
| if player_name == "player_0": | ||
| img_list.append(img) | ||
| # time.sleep(1) | ||
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| step += 1 | ||
| print("Total steps: {}".format(step)) | ||
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| save_path = "test.gif" | ||
| img_list = [Image.fromarray(img) for img in img_list] | ||
| img_list[0].save(save_path, save_all=True, append_images=img_list[1:], duration=500) | ||
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| def test_vec_env(): | ||
| from openrl.envs.common import make | ||
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| env = make( | ||
| "snakes_1v1", | ||
| opponent_wrappers=[ | ||
| RandomOpponentWrapper, | ||
| ], | ||
| env_wrappers=[ConvertObs], | ||
| render_mode="group_human", | ||
| env_num=2, | ||
| ) | ||
| obs, info = env.reset() | ||
| step = 0 | ||
| done = False | ||
| while not np.any(done): | ||
| action = env.random_action() | ||
| obs, reward, done, info = env.step(action) | ||
| time.sleep(0.3) | ||
| step += 1 | ||
| print("Total steps: {}".format(step)) | ||
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| if __name__ == "__main__": | ||
| test_vec_env() |
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