reduce workers for mixed and add sperate config for pure IL

baselines/custom_imitation_learning_rllib_tree_obs/ppo_imitation_tree_obs.yaml

@@ -9,7 +9,7 @@ flatland-sparse-small-tree-fc-apex-il-trainer:
     checkpoint_score_attr: episode_reward_mean
     num_samples: 3
     config:
-        num_workers: 13
+        num_workers: 6
         num_envs_per_worker: 5
         num_gpus: 0
         

baselines/custom_imitation_learning_rllib_tree_obs/pure_imitation_tree_obs.yaml

+flatland-sparse-small-tree-fc-apex-il-trainer:
+    run: ImitationAgent
+    env: flatland_sparse
+    stop:
+        timesteps_total: 15000000  # 1.5e7
+    checkpoint_freq: 50
+    checkpoint_at_end: True
+    keep_checkpoints_num: 50
+    checkpoint_score_attr: episode_reward_mean
+    num_samples: 3
+    config:
+        num_workers: 13
+        num_envs_per_worker: 5
+        num_gpus: 0
+        
+        clip_rewards: False
+        vf_clip_param: 500.0
+        entropy_coeff: 0.01
+        # effective batch_size: train_batch_size * num_agents_in_each_environment [5, 10]
+        # see https://github.com/ray-project/ray/issues/4628
+        train_batch_size: 1000  # 5000
+        rollout_fragment_length: 50  # 100
+        sgd_minibatch_size: 100  # 500
+        vf_share_layers: False
+
+        env_config:
+            observation: tree
+            observation_config:
+                max_depth: 2
+                shortest_path_max_depth: 30
+
+            generator: sparse_rail_generator
+            generator_config: small_v0
+
+            wandb:
+                project: flatland-paper
+                entity: aicrowd
+                tags: ["small_v0", "tree_obs", "apex_rllib_il"] # TODO should be set programmatically
+
+        model:
+            fcnet_activation: relu
+            fcnet_hiddens: [256, 256]
+            vf_share_layers: False  # Should be same as ppo vf_shared_layers