fixes acktr_cont issues

baselines/acktr/acktr_cont.py

@@ -46,7 +46,7 @@ def rollout(env, policy, max_pathlength, animate=False, obfilter=None):
             "action_dist": np.array(ac_dists), "logp" : np.array(logps)}
 
 def learn(env, policy, vf, gamma, lam, timesteps_per_batch, num_timesteps,
-    animate=False, callback=None, optimizer="adam", desired_kl=0.002):
+    animate=False, callback=None, desired_kl=0.002):
 
     obfilter = ZFilter(env.observation_space.shape)
 
@@ -117,14 +117,16 @@ def learn(env, policy, vf, gamma, lam, timesteps_per_batch, num_timesteps,
         # Policy update
         do_update(ob_no, action_na, standardized_adv_n)
 
+        min_stepsize = np.float32(1e-8)
+        max_stepsize = np.float32(1e0)
         # Adjust stepsize
         kl = policy.compute_kl(ob_no, oldac_dist)
         if kl > desired_kl * 2:
             logger.log("kl too high")
-            U.eval(tf.assign(stepsize, stepsize / 1.5))
+            U.eval(tf.assign(stepsize, tf.maximum(min_stepsize, stepsize / 1.5)))
         elif kl < desired_kl / 2:
             logger.log("kl too low")
-            U.eval(tf.assign(stepsize, stepsize * 1.5))
+            U.eval(tf.assign(stepsize, tf.minimum(max_stepsize, stepsize * 1.5)))
         else:
             logger.log("kl just right!")
 

baselines/acktr/run_mujoco.py

@@ -39,4 +39,4 @@ if __name__ == "__main__":
     parser.add_argument('--seed', help='RNG seed', type=int, default=0)
     parser.add_argument('--env', help='environment ID', type=str, default="Reacher-v1")
     args = parser.parse_args()
-    train(args.env_id, num_timesteps=1e6, seed=args.seed)
+    train(args.env, num_timesteps=1e6, seed=args.seed)

baselines/acktr/value_functions.py

@@ -13,7 +13,7 @@ class NeuralNetValueFunction(object):
         wd_dict = {}
         h1 = tf.nn.elu(dense(X, 64, "h1", weight_init=U.normc_initializer(1.0), bias_init=0, weight_loss_dict=wd_dict))
         h2 = tf.nn.elu(dense(h1, 64, "h2", weight_init=U.normc_initializer(1.0), bias_init=0, weight_loss_dict=wd_dict))
-        vpred_n = dense(h2, 1, "hfinal", weight_init=U.normc_initializer(1.0), bias_init=0, weight_loss_dict=wd_dict)[:,0]
+        vpred_n = dense(h2, 1, "hfinal", weight_init=None, bias_init=0, weight_loss_dict=wd_dict)[:,0]
         sample_vpred_n = vpred_n + tf.random_normal(tf.shape(vpred_n))
         wd_loss = tf.get_collection("vf_losses", None)
         loss = U.mean(tf.square(vpred_n - vtarg_n)) + tf.add_n(wd_loss)
@@ -22,7 +22,7 @@ class NeuralNetValueFunction(object):
         optim = kfac.KfacOptimizer(learning_rate=0.001, cold_lr=0.001*(1-0.9), momentum=0.9, \
                                     clip_kl=0.3, epsilon=0.1, stats_decay=0.95, \
                                     async=1, kfac_update=2, cold_iter=50, \
-                                    weight_decay_dict=wd_dict, max_grad_norm=None)
+                                    weight_decay_dict=wd_dict, max_grad_norm=1.0)
         vf_var_list = []
         for var in tf.trainable_variables():
             if "vf" in var.name: