Cartpole MAML + Discrete (#11028)

rllib/agents/maml/maml.py

@@ -55,25 +55,29 @@ DEFAULT_CONFIG = with_common_config({
     "maml_optimizer_steps": 5,
     # Inner Adaptation Step size
     "inner_lr": 0.1,
+    # Use Meta Env Template
+    "use_meta_env": True,
 })
 # __sphinx_doc_end__
 # yapf: enable
 
 
 # @mluo: TODO
-def set_worker_tasks(workers):
+def set_worker_tasks(workers, use_meta_env):
+    if use_meta_env:
         n_tasks = len(workers.remote_workers())
-    tasks = workers.local_worker().foreach_env(lambda x: x)[0].sample_tasks(
-        n_tasks)
+        tasks = workers.local_worker().foreach_env(lambda x: x)[
+            0].sample_tasks(n_tasks)
         for i, worker in enumerate(workers.remote_workers()):
             worker.foreach_env.remote(lambda env: env.set_task(tasks[i]))
 
 
 class MetaUpdate:
-    def __init__(self, workers, maml_steps, metric_gen):
+    def __init__(self, workers, maml_steps, metric_gen, use_meta_env):
         self.workers = workers
         self.maml_optimizer_steps = maml_steps
         self.metric_gen = metric_gen
+        self.use_meta_env = use_meta_env
 
     def __call__(self, data_tuple):
         # Metaupdate Step
@@ -91,7 +95,7 @@ class MetaUpdate:
         self.workers.sync_weights()
 
         # Set worker tasks
-        set_worker_tasks(self.workers)
+        set_worker_tasks(self.workers, self.use_meta_env)
 
         # Update KLS
         def update(pi, pi_id):
@@ -141,7 +145,8 @@ def execution_plan(workers, config):
     workers.sync_weights()
 
     # Samples and sets worker tasks
-    set_worker_tasks(workers)
+    use_meta_env = config["use_meta_env"]
+    set_worker_tasks(workers, use_meta_env)
 
     # Metric Collector
     metric_collect = CollectMetrics(
@@ -191,7 +196,8 @@ def execution_plan(workers, config):
 
     # Metaupdate Step
     train_op = rollouts.for_each(
-        MetaUpdate(workers, config["maml_optimizer_steps"], metric_collect))
+        MetaUpdate(workers, config["maml_optimizer_steps"], metric_collect,
+                   use_meta_env))
     return train_op
 
 

rllib/tuned_examples/maml/cartpole-maml.yaml

@@ -0,0 +1,27 @@
+# Same configs as Pendulum
+cartpole-maml:
+    env: CartPole-v0
+    run: MAML
+    stop:
+        training_iteration: 100   
+    config:
+        horizon: 200
+        rollout_fragment_length: 200
+        num_envs_per_worker: 10
+        inner_adaptation_steps: 1
+        maml_optimizer_steps: 5
+        gamma: 0.99
+        lambda: 1.0
+        lr: 0.001
+        vf_loss_coeff: 0.5
+        clip_param: 0.3
+        kl_target: 0.01
+        kl_coeff: 0.001
+        num_workers: 20
+        num_gpus: 1
+        inner_lr: 0.03
+        clip_actions: False
+        use_meta_env: False
+        model:
+            fcnet_hiddens: [64, 64]
+            free_log_std: True