4/23/2024 0 Comments Anylogic tutorial call center![]() Self.target_net.load_state_dict(self.policy_net.state_dict()) Self.target_net.load_state_dict(torch.load('target_net.pth')) Self.policy_net.load_state_dict(torch.load('policy_net.pth')) Self.target_net = DQN(device=vice).to(vice) Self.policy_net = DQN(device=vice).to(vice) With open("step.json", "r") as read_content: Self.reward_buffer = json.load(read_content) With open("reward_buffer.json", "r") as read_content: Self.replay_buffer = json.load(read_content) With open("replay_buffer.json", "r") as read_content: vice = vice("cuda" if _available() else "cpu") The taxi receives a -1 reward, unless one of the above-mentioned rewards is triggered. When the taxi successfully drops off the passenger, it receives a reward of +20. ![]() When the taxi makes a failed pick up or drop off, it receives a reward of -10. The state space is the position of taxi on the x-axis, the position of the taxi on the y-axis, and whether the passenger has been picked up (0 or 1). The action space in this environment is 0: move up, 1: move down, 2: move left, 3: move right, 4: pick up, and 5: drop off. Once the passenger is dropped off or more than 200 action steps are taken, the episode ends. ![]() The goal of the taxi is to first pick up the passenger and then drop the passenger off at the destination. The taxi will be initialized anywhere randomly other than the passenger location. The initial location of the passenger is G, and the destination of the passenger is Y. A visualization of the grid world is shown in figure 1, where the green lines represent walls that the taxi cannot go across. ![]() This environment is in a 4*4 grid world, with an RL-controlled taxi and a passenger. This method can also be used for RL (not deep) training, but due to simplicity, most environments that can be solved with RL can be simulated directly in programming languages, like Python. In this blog post, we introduce a new way to apply DRL to simulation models in AnyLogic using the Pypeline library in AnyLogic. Unfortunately, it is still not stable enough to handle complicated simulation models. The newly developed Alpyne library is a Python library that enables users to train DRL agents in Python by interacting with the AnyLogic model during run time. AnyLogic is a perfect platform for building simulation models to train DRL agents in complex environments. However, using Python, as a programming language, to build large-scale simulations that simulate complicated environments is hard. With a diverse library of machine learning tools, Python has become the go-to choice for DRL training. Since solving most reinforcement learning problems requires an extremely large amount of data, most DRL (or RL) agents are trained in a simulated environment. With the ability of deep learning to handle the continuous or complicated state space and the ability of reinforcement learning to learn from trial and error in a complicated environment, DRL is particularly good at solving problems that lack good exact or heuristic methods in complex environments. ![]() The rapid development of deep reinforcement learning (DRL), the combination of deep learning and reinforcement learning, has attracted more and more researchers from different fields to apply DRL to solve problems in their research fields. ![]()
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