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Attempt to fix loss of perf because of VecEnvs

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This commit is contained in:
Antonin Raffin 2019-09-20 18:06:08 +02:00
parent 0e727a5f72
commit a9b8276efb
2 changed files with 11 additions and 11 deletions
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4
tests/test_run.py
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@ -7,7 +7,7 @@ from torchy_baselines import TD3, CEMRL, PPO
def test_td3(): def test_td3():
model = TD3('MlpPolicy', 'Pendulum-v0', policy_kwargs=dict(net_arch=[64, 64]), model = TD3('MlpPolicy', 'Pendulum-v0', policy_kwargs=dict(net_arch=[64, 64]),
start_timesteps=100, verbose=1, create_eval_env=True) start_timesteps=100, verbose=1, create_eval_env=True)
model.learn(total_timesteps=500, eval_freq=100) model.learn(total_timesteps=20000, eval_freq=1000)
model.save("test_save") model.save("test_save")
model.load("test_save") model.load("test_save")
os.remove("test_save.pth") os.remove("test_save.pth")
@ -15,7 +15,7 @@ def test_td3():
def test_cemrl(): def test_cemrl():
model = CEMRL('MlpPolicy', 'Pendulum-v0', policy_kwargs=dict(net_arch=[16]), pop_size=2, n_grad=1, model = CEMRL('MlpPolicy', 'Pendulum-v0', policy_kwargs=dict(net_arch=[16]), pop_size=2, n_grad=1,
start_timesteps=100, verbose=1, create_eval_env=True) start_timesteps=100, verbose=1, create_eval_env=True)
model.learn(total_timesteps=1000, eval_freq=500) model.learn(total_timesteps=20000, eval_freq=1000)
model.save("test_save") model.save("test_save")
model.load("test_save") model.load("test_save")
os.remove("test_save.pth") os.remove("test_save.pth")

18
torchy_baselines/common/buffers.py
View file

@ -68,11 +68,11 @@ class ReplayBuffer(BaseBuffer):
def add(self, state, next_state, action, reward, done): def add(self, state, next_state, action, reward, done):
# Copy to avoid modification by reference # Copy to avoid modification by reference
self.states[self.pos] = th.FloatTensor(np.array(state)) self.states[self.pos] = th.FloatTensor(np.array(state).copy())
self.next_states[self.pos] = th.FloatTensor(np.array(next_state)) self.next_states[self.pos] = th.FloatTensor(np.array(next_state).copy())
self.actions[self.pos] = th.FloatTensor(np.array(action)) self.actions[self.pos] = th.FloatTensor(np.array(action).copy())
self.rewards[self.pos] = th.FloatTensor(np.array(reward)) self.rewards[self.pos] = th.FloatTensor(np.array(reward).copy())
self.dones[self.pos] = th.FloatTensor(np.array(done)) self.dones[self.pos] = th.FloatTensor(np.array(done).copy())
self.pos += 1 self.pos += 1
if self.pos == self.buffer_size: if self.pos == self.buffer_size:
@ -131,10 +131,10 @@ class RolloutBuffer(BaseBuffer):
def add(self, state, action, reward, done, value, log_prob): def add(self, state, action, reward, done, value, log_prob):
self.values[self.pos] = th.FloatTensor(value.clone().cpu().flatten()) self.values[self.pos] = th.FloatTensor(value.clone().cpu().flatten())
self.log_probs[self.pos] = th.FloatTensor(log_prob.cpu().clone()) self.log_probs[self.pos] = th.FloatTensor(log_prob.cpu().clone())
self.states[self.pos] = th.FloatTensor(np.array(state)) self.states[self.pos] = th.FloatTensor(np.array(state).copy())
self.actions[self.pos] = th.FloatTensor(np.array(action)) self.actions[self.pos] = th.FloatTensor(np.array(action).copy())
self.rewards[self.pos] = th.FloatTensor(np.array(reward)) self.rewards[self.pos] = th.FloatTensor(np.array(reward).copy())
self.dones[self.pos] = th.FloatTensor(np.array(done)) self.dones[self.pos] = th.FloatTensor(np.array(done).copy())
self.pos += 1 self.pos += 1
if self.pos == self.buffer_size: if self.pos == self.buffer_size:
self.full = True self.full = True