def train_single_player_return():
max_episodes = 50000
episodes_per_update = 10
render = False
gamma = 0.99
lr = 0.005
betas = (0.9, 0.999)
path = 'models/single_player.pkl'
env = SinglePlayerReturn(DiscreteActionBotSim())
policy = ActorCritic()
optimizer = optim.Adam(policy.parameters(), lr=lr, betas=betas)
r = []
i_episode = 0
while i_episode < max_episodes:
state = env.reset()
done = False
while not done:
action = policy(state)
state, reward, done, i = env.step(action)
policy.temp_rewards.append(reward)
i_episode += 1
r.append(reward)
if i_episode % 10:
policy.updateMemory(gamma)
continue
# Updating the policy :
optimizer.zero_grad()
loss = policy.calculateLoss()
loss.backward()
policy.clip_grads()
optimizer.step()
policy.clearMemory()
if i_episode % 500 == 0:
if len(r) > 50:
print('Episode ', i_episode, ': avg reward ', sum(r) / len(r))
else:
print('Episode ', i_episode, ': avg reward n/a')
if i_episode % 1000 == 0:
torch.save(policy, path)
if len(r) > 100:
r.pop(0)
if sum(r) / len(r) > 0.8:
r = []
v += 1
print('CONVERGED v', v)
break
torch.save(policy, path)
def train():
# Defaults parameters:
# gamma = 0.99
# lr = 0.02
# betas = (0.9, 0.999)
# random_seed = 543
render = False
gamma = 0.99
lr = 0.02
betas = (0.9, 0.999)
random_seed = 543
torch.manual_seed(random_seed)
env = gym.make('LunarLander-v2')
env.seed(random_seed)
policy = ActorCritic()
optimizer = optim.Adam(policy.parameters(), lr=lr, betas=betas)
print(lr,betas)
running_reward = 0
for i_episode in range(0, 10000):
state = env.reset()
for t in range(10000):
action = policy(state)
state, reward, done, _ = env.step(action)
policy.rewards.append(reward)
running_reward += reward
if render and i_episode > 1000:
env.render()
if done:
break
# Updating the policy :
optimizer.zero_grad()
loss = policy.calculateLoss(gamma)
loss.backward()
optimizer.step()
policy.clearMemory()
# saving the model if episodes > 999 OR avg reward > 200
#if i_episode > 999:
# torch.save(policy.state_dict(), './preTrained/LunarLander_{}_{}_{}.pth'.format(lr, betas[0], betas[1]))
if running_reward > 4000:
torch.save(policy.state_dict(), './preTrained/LunarLander_{}_{}_{}.pth'.format(lr, betas[0], betas[1]))
print("########## Solved! ##########")
test(name='LunarLander_{}_{}_{}.pth'.format(lr, betas[0], betas[1]))
break
if i_episode % 20 == 0:
running_reward = running_reward/20
print('Episode {}\tlength: {}\treward: {}'.format(i_episode, t, running_reward))
running_reward = 0
