drl-supply-chain-hilo-dqn-sim.py

policy_net = PolicyNetworkDQN(2*T, len(price_grid)).to(device)
target_net = PolicyNetworkDQN(2*T, len(price_grid)).to(device)
optimizer = optim.AdamW(policy_net.parameters(), lr = 0.005)
policy = AnnealedEpsGreedyPolicy()
memory = ReplayMemory(10000)

TARGET_UPDATE = 20

num_episodes = 1000
return_trace = []
p_trace = []              # Price schedules used in each episode
for i_episode in range(num_episodes):
    state = env_intial_state()
    reward_trace = []
    p = []
    for t in range(T):
        # Select and perform an action
        with torch.no_grad():
          q_values = policy_net(to_tensor(state))
        action = policy.select_action(q_values.detach().numpy())

        next_state, reward = env_step(t, state, action)

        # Store the transition in memory
        memory.push(to_tensor(state), 
                    to_tensor_long(action), 
                    to_tensor(next_state) if t != T - 1 else None, 
                    to_tensor([reward]))

        # Move to the next state
        state = next_state

        # Perform one step of the optimization
        update_model(memory, policy_net, target_net)

        reward_trace.append(reward)
        p.append(price_grid[action])

    return_trace.append(sum(reward_trace))
    p_trace.append(p)

    # Update the target network, copying all weights and biases in DQN
    if i_episode % TARGET_UPDATE == 0:
        target_net.load_state_dict(policy_net.state_dict())