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68 lines
2.3 KiB
68 lines
2.3 KiB
# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""Evaluation for DQN"""
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import argparse
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import gym
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from mindspore import context
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from mindspore.common import set_seed
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from src.config import config_dqn as cfg
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from src.agent import Agent
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parser = argparse.ArgumentParser(description='MindSpore dqn Example')
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parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU'],
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help='device where the code will be implemented (default: Ascend)')
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parser.add_argument('--ckpt_path', type=str, default=None, help='if is test, must provide\
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path where the trained ckpt file')
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args = parser.parse_args()
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set_seed(1)
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if __name__ == "__main__":
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context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
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env = gym.make('CartPole-v1')
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cfg.state_space_dim = env.observation_space.shape[0]
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cfg.action_space_dim = env.action_space.n
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agent = Agent(**cfg)
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# load checkpoint
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if args.ckpt_path:
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param_dict = load_checkpoint(args.ckpt_path)
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not_load_param = load_param_into_net(agent.policy_net, param_dict)
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if not_load_param:
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raise ValueError("Load param into net fail!")
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score = 0
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agent.load_dict()
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for episode in range(50):
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s0 = env.reset()
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total_reward = 1
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while True:
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a0 = agent.eval_act(s0)
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s1, r1, done, _ = env.step(a0)
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if done:
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r1 = -1
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if done:
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break
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total_reward += r1
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s0 = s1
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score += total_reward
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print("episode", episode, "total_reward", total_reward)
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print("mean_reward", score/50)
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