Training Process for A2C model.

train_process_for_a2c_model.py

# Set up training process.
from collections import deque

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

agent_a2c = A2CModel().to(device)
optimizer = optim.Adam(agent_a2c.parameters(), lr=0.00015)

env_info = env.reset(train_mode=True)[brain_name] 
states = env_info.vector_observations
init_states = states

n_episodes = 1
n_steps = 10
episode_end = False
a2c_ep_rewards_list = []
ep_rewards_deque = deque([0], maxlen=100) # initialize with 0
ep_rewards = 0

while True:
    batch_s, batch_a, batch_v_t, accu_rewards, init_states, episode_end = collect_trajectories(
        agent_a2c, env, brain_name, init_states, episode_end, n_steps)

    loss, mus, stds = learn(batch_s, batch_a, batch_v_t, agent_a2c, optimizer)
    ep_rewards += accu_rewards
    print('\rEpisode {:>4}\tEpisodic Score {:>7.3f}\tLoss {:>12.6f}'.format(
        n_episodes, np.mean(ep_rewards_deque), float(loss)), end="")

    if episode_end == True:
        if n_episodes % 100 == 0:
            print('\rEpisode {:>4}\tEpisodic Score {:>7.3f}\tLoss {:>12.6f}'.format(
                n_episodes, np.mean(ep_rewards_deque), float(loss)))

        if np.mean(ep_rewards_deque) >= 34:
            break
        a2c_ep_rewards_list.append(ep_rewards/num_agents)
        ep_rewards_deque.append(ep_rewards/num_agents)
        ep_rewards = 0
        n_episodes += 1
        episode_end = False


# save a2c model
pth = './checkpoint/a2c_checkpoint.pth'
torch.save(agent_a2c.state_dict(), pth)

a2c_ep_rewards_list = np.array(a2c_ep_rewards_list)
np.save('./data/a2c_ep_rewards_list.npy', a2c_ep_rewards_list)