Merge pull request #153 from rail-berkeley/iql-test

Modify IQL test.

examples/iql/mujoco_finetune.py

@@ -84,7 +84,8 @@ def main():
         variant=variant,
         exp_prefix='iql-halfcheetah-medium-v2',
         mode="here_no_doodad",
-        unpack_variant=False
+        unpack_variant=False,
+        use_gpu=False,
     )
 
 if __name__ == "__main__":

rlkit/testing/debug_util.py

@@ -0,0 +1,24 @@
+"""For tracing programs and comparing outputs"""
+
+import torch
+
+i = 0
+
+def save(x):
+    torch.save(x, "../tmp.pt")
+    return x
+
+def load():
+    return torch.load("../tmp.pt")
+
+def savei(x):
+    global i
+    torch.save(x, "../tmp/%d.pt" % i)
+    i = i + 1
+    return x
+
+def loadi():
+    global i
+    x = torch.load("../tmp/%d.pt" % i)
+    i = i + 1
+    return x

rlkit/torch/sac/iql_trainer.py

@@ -139,7 +139,6 @@ def train_from_torch(self, batch, train=True, pretrain=False,):
         Policy and Alpha Loss
         """
         dist = self.policy(obs)
-        new_obs_actions, log_pi = dist.rsample_and_logprob()
 
         """
         QF Loss
@@ -237,10 +236,6 @@ def train_from_torch(self, batch, train=True, pretrain=False,):
                 'Q Targets',
                 ptu.get_numpy(q_target),
             ))
-            self.eval_statistics.update(create_stats_ordered_dict(
-                'Log Pis',
-                ptu.get_numpy(log_pi),
-            ))
             self.eval_statistics.update(create_stats_ordered_dict(
                 'rewards',
                 ptu.get_numpy(rewards),

tests/regression/iql/halfcheetah_offline_progress.csv

@@ -1,3 +1,3 @@
 epoch,eval/num paths total,eval/num steps total,expl/Actions Max,expl/Actions Mean,expl/Actions Min,expl/Actions Std,expl/Average Returns,expl/Num Paths,expl/Returns Max,expl/Returns Mean,expl/Returns Min,expl/Returns Std,expl/Rewards Max,expl/Rewards Mean,expl/Rewards Min,expl/Rewards Std,expl/num paths total,expl/num steps total,expl/path length Max,expl/path length Mean,expl/path length Min,expl/path length Std,replay_buffer/size,time/epoch_time (s),time/evaluation sampling (s),time/exploration sampling (s),time/global_time (s),time/replay buffer data storing (s),time/saving (s),time/training (s),trainer/Advantage Score Max,trainer/Advantage Score Mean,trainer/Advantage Score Min,trainer/Advantage Score Std,trainer/Advantage Weights Max,trainer/Advantage Weights Mean,trainer/Advantage Weights Min,trainer/Advantage Weights Std,trainer/Policy Loss,trainer/Q Targets Max,trainer/Q Targets Mean,trainer/Q Targets Min,trainer/Q Targets Std,trainer/Q1 Predictions Max,trainer/Q1 Predictions Mean,trainer/Q1 Predictions Min,trainer/Q1 Predictions Std,trainer/Q2 Predictions Max,trainer/Q2 Predictions Mean,trainer/Q2 Predictions Min,trainer/Q2 Predictions Std,trainer/QF1 Loss,trainer/QF2 Loss,trainer/V1 Predictions Max,trainer/V1 Predictions Mean,trainer/V1 Predictions Min,trainer/V1 Predictions Std,trainer/VF Loss,trainer/num train calls,trainer/policy/mean Max,trainer/policy/mean Mean,trainer/policy/mean Min,trainer/policy/mean Std,trainer/policy/std Max,trainer/policy/std Mean,trainer/policy/std Min,trainer/policy/std Std,trainer/replay_buffer_len,trainer/rewards Max,trainer/rewards Mean,trainer/rewards Min,trainer/rewards Std,trainer/terminals Max,trainer/terminals Mean,trainer/terminals Min,trainer/terminals Std
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--1,0,0,0.05840967,-0.0072007906,-0.09415653,0.046789993,0.5074781763310496,1,0.5074781763310496,0.5074781763310496,0.5074781763310496,0.0,0.2553473427685612,0.2537390881655248,0.2521308335624885,0.0016082546030363465,2,4,2,2.0,2,0.0,998999,0.14186596870422363,0.0059931278228759766,0.013265132904052734,32.5822057723999,2.1457672119140625e-06,0.012963533401489258,0.10884451866149902,0.0034906738,0.0020773786,0.0006640834,0.0014132953,1.010527,1.0062606,1.0019943,0.0042663813,874.27075,0.8171325,0.81321764,0.80930287,0.0039148033,0.00060014235,-0.0017158713,-0.004031885,0.0023160137,0.0006001451,0.0006001451,0.0006001451,0.0,0.66411924,0.66036254,-0.0006595902,-0.0020728854,-0.0034861807,0.0014132953,4.4190338e-06,4,0.008797872,-0.01306281,-0.01957312,0.0073613864,0.049831923,0.049831625,0.049831126,3.505173e-07,998999,0.8163113,0.8157928,0.81527424,0.0005185306,0.0,0.0,0.0,0.0
+-2,0,0,0.13717124,0.03626487,-0.049319427,0.045948837,-0.19655459402248504,1,-0.19655459402248504,-0.19655459402248504,-0.19655459402248504,0.0,-0.09325349516403507,-0.09827729701124252,-0.10330109885844999,0.005023801847207458,1,2,2,2.0,2,0.0,998999,0.7398281097412109,0.022743940353393555,0.0016701221466064453,20.234392166137695,1.9073486328125e-06,0.008835077285766602,0.7058250904083252,-0.0036168185,-0.008904904,-0.01419299,0.005288086,0.9892082,0.9737615,0.9583148,0.0154467225,857.7699,1.0095485,0.91235673,0.8151649,0.09719181,-3.695485e-05,-0.0023042315,-0.004571508,0.0022672766,0.0,0.0,0.0,0.0,0.8464968,0.84184104,0.01419299,0.007483647,0.0007743044,0.006709343,3.2178352e-05,100,0.004211932,0.0006116896,-0.0027325656,0.0019366256,0.049787067,0.049787063,0.049787067,3.7252903e-09,998999,1.0072821,0.90650094,0.80571973,0.1007812,0.0,0.0,0.0,0.0
+-1,0,0,0.049955986,-0.051808134,-0.24854904,0.08398724,0.4338117202694962,1,0.4338117202694962,0.4338117202694962,0.4338117202694962,0.0,0.24300404877931653,0.2169058601347481,0.19080767149017966,0.026098188644568435,2,4,2,2.0,2,0.0,998999,0.6178200244903564,0.0024394989013671875,0.002116680145263672,20.855294704437256,1.6689300537109375e-06,0.008217334747314453,0.6044015884399414,-0.004432007,-0.0058829254,-0.0073338435,0.0014509181,0.98679197,0.98251534,0.97823876,0.0042766035,440.564,1.0878772,0.8795528,0.6712284,0.20832437,0.07119025,0.055561554,0.039932854,0.0156287,0.06366714,0.046736993,0.029806845,0.016930148,0.7160932,0.7302138,0.013697105,0.0122417575,0.0107864095,0.001455348,1.1014193e-05,200,-0.17424563,-0.48487774,-0.8831331,0.2023349,0.051678017,0.05148844,0.051258426,0.00012519717,998999,1.0813023,0.8714981,0.661694,0.20980415,0.0,0.0,0.0,0.0

tests/regression/iql/halfcheetah_online_progress.csv

@@ -1,5 +1,5 @@
-Epoch,epoch,eval/num paths total,eval/num steps total,expl/Actions Max,expl/Actions Mean,expl/Actions Min,expl/Actions Std,expl/Average Returns,expl/Num Paths,expl/Returns Max,expl/Returns Mean,expl/Returns Min,expl/Returns Std,expl/Rewards Max,expl/Rewards Mean,expl/Rewards Min,expl/Rewards Std,expl/env_infos/final/reward_ctrl Max,expl/env_infos/final/reward_ctrl Mean,expl/env_infos/final/reward_ctrl Min,expl/env_infos/final/reward_ctrl Std,expl/env_infos/final/reward_run Max,expl/env_infos/final/reward_run Mean,expl/env_infos/final/reward_run Min,expl/env_infos/final/reward_run Std,expl/env_infos/initial/reward_ctrl Max,expl/env_infos/initial/reward_ctrl Mean,expl/env_infos/initial/reward_ctrl Min,expl/env_infos/initial/reward_ctrl Std,expl/env_infos/initial/reward_run Max,expl/env_infos/initial/reward_run Mean,expl/env_infos/initial/reward_run Min,expl/env_infos/initial/reward_run Std,expl/env_infos/reward_ctrl Max,expl/env_infos/reward_ctrl Mean,expl/env_infos/reward_ctrl Min,expl/env_infos/reward_ctrl Std,expl/env_infos/reward_run Max,expl/env_infos/reward_run Mean,expl/env_infos/reward_run Min,expl/env_infos/reward_run Std,expl/num paths total,expl/num steps total,expl/path length Max,expl/path length Mean,expl/path length Min,expl/path length Std,replay_buffer/size,time/data storing (s),time/epoch (s),time/evaluation sampling (s),time/exploration sampling (s),time/logging (s),time/saving (s),time/total (s),time/training (s),trainer/Advantage Score Max,trainer/Advantage Score Mean,trainer/Advantage Score Min,trainer/Advantage Score Std,trainer/Advantage Weights Max,trainer/Advantage Weights Mean,trainer/Advantage Weights Min,trainer/Advantage Weights Std,trainer/Log Pis Max,trainer/Log Pis Mean,trainer/Log Pis Min,trainer/Log Pis Std,trainer/Policy Loss,trainer/Q Targets Max,trainer/Q Targets Mean,trainer/Q Targets Min,trainer/Q Targets Std,trainer/Q1 Predictions Max,trainer/Q1 Predictions Mean,trainer/Q1 Predictions Min,trainer/Q1 Predictions Std,trainer/Q2 Predictions Max,trainer/Q2 Predictions Mean,trainer/Q2 Predictions Min,trainer/Q2 Predictions Std,trainer/QF1 Loss,trainer/QF2 Loss,trainer/V1 Predictions Max,trainer/V1 Predictions Mean,trainer/V1 Predictions Min,trainer/V1 Predictions Std,trainer/VF Loss,trainer/num train calls,trainer/policy/mean Max,trainer/policy/mean Mean,trainer/policy/mean Min,trainer/policy/mean Std,trainer/policy/std Max,trainer/policy/std Mean,trainer/policy/std Min,trainer/policy/std Std,trainer/replay_buffer_len,trainer/rewards Max,trainer/rewards Mean,trainer/rewards Min,trainer/rewards Std,trainer/terminals Max,trainer/terminals Mean,trainer/terminals Min,trainer/terminals Std
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