SAC Performance Fixes (#6295)

* SAC Performance Fixes

* Small Changes

* Update sac_model.py

* fix normalize wrapper

* Update test_eager_support.py

Co-authored-by: Eric Liang <ekhliang@gmail.com>

rllib/tuned_examples/pendulum-sac.yaml

@@ -0,0 +1,37 @@
+# Pendulum SAC can attain -150+ reward in 6-7k
+# Configurations are the similar to original softlearning/sac codebase
+pendulum_sac:
+    env: Pendulum-v0
+    run: SAC
+    stop: 
+        episode_reward_mean: -150
+    config:
+        horizon: 200
+        soft_horizon: False
+        Q_model:
+          hidden_activation: relu
+          hidden_layer_sizes: [256, 256]
+        policy_model:
+          hidden_activation: relu
+          hidden_layer_sizes: [256, 256]
+        tau: 0.005
+        target_entropy: auto
+        no_done_at_end: True
+        n_step: 1
+        sample_batch_size: 1
+        prioritized_replay: False
+        train_batch_size: 256
+        target_network_update_freq: 1
+        timesteps_per_iteration: 1000
+        learning_starts: 256
+        exploration_enabled: True
+        optimization:
+          actor_learning_rate: 0.0003
+          critic_learning_rate: 0.0003
+          entropy_learning_rate: 0.0003
+        num_workers: 0
+        num_gpus: 0
+        clip_actions: False
+        normalize_actions: True
+        evaluation_interval: 1
+        metrics_smoothing_episodes: 5

rllib/tuned_examples/regression_tests/pendulum-sac.yaml

@@ -3,8 +3,10 @@ pendulum-sac:
     run: SAC
     stop:
         episode_reward_mean: -300  # note that evaluation perf is higher
-        timesteps_total: 15000
+        timesteps_total: 10000
     config:
         evaluation_interval: 1  # logged under evaluation/* metric keys
-        soft_horizon: true
-        metrics_smoothing_episodes: 10
+        soft_horizon: True
+        clip_actions: False
+        normalize_actions: True
+        metrics_smoothing_episodes: 5