if __name__ == "__main__":
batch_size = 256
gamma = 0.999
eps_start = 1
eps_end = 0.01
eps_decay = 0.001
target_update = 10
memory_size = 100000
lr = 0.001
num_episodes = 1000
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
env_manager = EnvironmentManager(device)
strategy = EpsilonGreedyStrategy(eps_start, eps_end, eps_decay)
agent = Agent(strategy, env_manager.num_actions_available(), device)
memory = ReplayMemory(memory_size)
input_shape = (3, 60, 40)
n_actions = 4
policy_net = DQN(input_shape, n_actions).to(device)
target_net = DQN(input_shape, n_actions).to(device)
target_net.load_state_dict(policy_net.state_dict())
target_net.eval() # Switch target to inference mode
optimizer = optim.Adam(params=policy_net.parameters(), lr=lr)
train(num_episodes, env_manager, agent, policy_net, target_net, memory,
batch_size, gamma, optimizer, target_update)
