add posibility to add stats

AcutronicRobotics · Apr 5, 2019 · 5205590 · 5205590
1 parent a3d2ca3
commit 5205590
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Showing 3 changed files with 16 additions and 10 deletions.
diff --git a/baselines/ppo2/defaults.py b/baselines/ppo2/defaults.py
@@ -36,8 +36,8 @@ def mara_mlp():
         log_interval = 1,
         ent_coef = 0.0,
         lr = lambda f: 3e-3 * f,
-        cliprange = 0.2,
-        vf_coef = 0.5,
+        cliprange = 0.25,
+        vf_coef = 1,
         max_grad_norm = 0.5,
         seed = 0,
         value_network = 'copy',
@@ -51,7 +51,7 @@ def mara_mlp():
         # env_name = 'MARACollisionOrient-v0',
         transfer_path = None,
         # transfer_path = '/tmp/ros2learn/MARA-v0/ppo2_mlp/2019-02-19_12h47min/checkpoints/best',
-        trained_path = '/tmp/ros2learn/MARAOrient-v0/ppo2_mlp/2019-03-26_14h27min/checkpoints/best'
+        trained_path = '/tmp/ros2learn/MARA-v0/ppo2_mlp/2019-04-02_13h18min/checkpoints/best'
     )
 
 def mara_lstm():
@@ -79,8 +79,8 @@ def mara_lstm():
         network = 'lstm',
         total_timesteps = 1e8,
         save_interval = 10,
-        env_name = 'MARACollisionOrientRandomTarget-v0',
-        num_envs = 2,
+        env_name = 'MARA-v0',
+        num_envs = 4,
         transfer_path = None,
         # transfer_path = '/tmp/ros2learn/MARACollisionOrientRandomTarget-v0/ppo2_lstm/checkpoints/00090',
         trained_path = '/tmp/ros2learn/MARACollisionOrientRandomTarget-v0/ppo2_lstm/checkpoints/00090'

diff --git a/baselines/ppo2/ppo2.py b/baselines/ppo2/ppo2.py
@@ -138,6 +138,7 @@ def learn(*, network, env, total_timesteps, eval_env = None, seed=None, nsteps=2
         cliprangenow = cliprange(frac)
         # Get minibatch
         obs, returns, masks, actions, values, neglogpacs, states, epinfos = runner.run() #pylint: disable=E0632
+
         if eval_env is not None:
             eval_obs, eval_returns, eval_masks, eval_actions, eval_values, eval_neglogpacs, eval_states, eval_epinfos = eval_runner.run() #pylint: disable=E0632
 
@@ -194,7 +195,7 @@ def learn(*, network, env, total_timesteps, eval_env = None, seed=None, nsteps=2
             logger.logkv("fps", fps)
             logger.logkv("explained_variance", float(ev))
             mean_rewbuffer = safemean([epinfo['r'] for epinfo in epinfobuf])
-            logger.logkv('eprewmean', mean_rewbuffer)
+            logger.logkv('eprewmean_smooth', mean_rewbuffer)
             logger.logkv('eprewsem', np.std([epinfo['r'] for epinfo in epinfobuf]))
             logger.logkv('eplenmean', safemean([epinfo['l'] for epinfo in epinfobuf]))
             if eval_env is not None:
@@ -203,6 +204,11 @@ def learn(*, network, env, total_timesteps, eval_env = None, seed=None, nsteps=2
             logger.logkv('time_elapsed', tnow - tfirststart)
             for (lossval, lossname) in zip(lossvals, model.loss_names):
                 logger.logkv(lossname, lossval)
+
+            key_set = [key for key in list(epinfobuf)[0].keys() if key not in ["r", "l", "t"]]
+            for key in key_set:
+                logger.logkv(key, list(epinfobuf)[-1][key])
+
             if MPI is None or MPI.COMM_WORLD.Get_rank() == 0:
                 logger.dumpkvs()
 

diff --git a/baselines/ppo2/runner.py b/baselines/ppo2/runner.py
@@ -22,6 +22,7 @@ def run(self):
         mb_obs, mb_rewards, mb_actions, mb_values, mb_dones, mb_neglogpacs = [],[],[],[],[],[]
         mb_states = self.states
         epinfos = []
+        maybeepinfo = []
         # For n in range number of steps
         for _ in range(self.nsteps):
             # Given observations, get action value and neglopacs
@@ -37,8 +38,9 @@ def run(self):
             # Infos contains a ton of useful informations
             self.obs[:], rewards, self.dones, infos = self.env.step(actions)
             for info in infos:
-                maybeepinfo = info.get('episode')
-                if maybeepinfo: epinfos.append(maybeepinfo)
+                for key in info.keys():
+                    maybeepinfo.append(info.get(key))
+                if maybeepinfo: epinfos.append({key:dict[key] for dict in maybeepinfo for key in dict})
             mb_rewards.append(rewards)
         #batch of steps to batch of rollouts
         mb_obs = np.asarray(mb_obs, dtype=self.obs.dtype)
@@ -72,5 +74,3 @@ def sf01(arr):
     """
     s = arr.shape
     return arr.swapaxes(0, 1).reshape(s[0] * s[1], *s[2:])
-
-