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Tune parameters for DDPG

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Shangtong Zhang
2018-04-06 10:34:52 -06:00
parent 5fbc1e2e3f
commit 4c6481d5be
3 changed files with 20 additions and 28 deletions
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agent/DDPG_agent.py
+7 -5
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@@ -29,8 +29,8 @@ class DDPGAgent(BaseAgent):
self.criterion = nn.MSELoss()
self.total_steps = 0
self.state_normalizer = Normalizer(self.task.state_dim)
self.reward_normalizer = Normalizer(1)
# self.state_normalizer = Normalizer(self.task.state_dim)
# self.reward_normalizer = Normalizer(1)
def soft_update(self, target, src):
for target_param, param in zip(target.parameters(), src.parameters()):
@@ -40,7 +40,7 @@ class DDPGAgent(BaseAgent):
def episode(self, deterministic=False, video_recorder=None):
self.random_process.reset_states()
state = self.task.reset()
state = self.state_normalizer(state)
# state = self.state_normalizer(state)
config = self.config
actor = self.worker_network.actor
@@ -59,9 +59,9 @@ class DDPGAgent(BaseAgent):
next_state, reward, done, info = self.task.step(action)
if video_recorder is not None:
video_recorder.capture_frame()
next_state = self.state_normalizer(next_state)
# next_state = self.state_normalizer(next_state)
total_reward += reward
reward = self.reward_normalizer(reward)
# reward = self.reward_normalizer(reward)
if not deterministic:
self.replay.feed([state, action, reward, next_state, int(done)])
@@ -99,6 +99,8 @@ class DDPGAgent(BaseAgent):
actor.zero_grad()
self.actor_opt.zero_grad()
actions.backward(-var_actions.grad.data)
for param in actor.parameters():
param.grad.data.clamp(-config.gradient_clip, config.gradient_clip)
self.actor_opt.step()
self.soft_update(self.target_network, self.worker_network)