[RLlib; new API stack by default] Switch on new API stack by default for SAC and DQN.

doc/source/rllib/doc_code/checkpoints.py

@@ -4,12 +4,12 @@
 import tempfile
 
 from ray.rllib.algorithms.algorithm import Algorithm
-from ray.rllib.algorithms.dqn import DQNConfig
+from ray.rllib.algorithms.ppo import PPOConfig
 from ray.rllib.utils.checkpoints import convert_to_msgpack_checkpoint
 
 
 # Base config used for both pickle-based checkpoint and msgpack-based one.
-config = DQNConfig().environment("CartPole-v1")
+config = PPOConfig().environment("CartPole-v1").env_runners(num_env_runners=0)
 # Build algorithm object.
 algo1 = config.build()
 

doc/source/rllib/doc_code/replay_buffer_demo.py

@@ -15,13 +15,31 @@
 
 
 # __sphinx_doc_replay_buffer_type_specification__begin__
-config = DQNConfig().training(replay_buffer_config={"type": ReplayBuffer})
+config = (
+    DQNConfig()
+    .api_stack(
+        enable_env_runner_and_connector_v2=False, enable_rl_module_and_learner=False
+    )
+    .training(replay_buffer_config={"type": ReplayBuffer})
+)
 
-another_config = DQNConfig().training(replay_buffer_config={"type": "ReplayBuffer"})
+another_config = (
+    DQNConfig()
+    .api_stack(
+        enable_env_runner_and_connector_v2=False, enable_rl_module_and_learner=False
+    )
+    .training(replay_buffer_config={"type": "ReplayBuffer"})
+)
 
 
-yet_another_config = DQNConfig().training(
-    replay_buffer_config={"type": "ray.rllib.utils.replay_buffers.ReplayBuffer"}
+yet_another_config = (
+    DQNConfig()
+    .api_stack(
+        enable_env_runner_and_connector_v2=False, enable_rl_module_and_learner=False
+    )
+    .training(
+        replay_buffer_config={"type": "ray.rllib.utils.replay_buffers.ReplayBuffer"}
+    )
 )
 
 validate_buffer_config(config)
@@ -75,13 +93,16 @@ def sample(
 
 config = (
     DQNConfig()
-    .training(replay_buffer_config={"type": LessSampledReplayBuffer})
+    .api_stack(
+        enable_env_runner_and_connector_v2=False, enable_rl_module_and_learner=False
+    )
     .environment(env="CartPole-v1")
+    .training(replay_buffer_config={"type": LessSampledReplayBuffer})
 )
 
 tune.Tuner(
     "DQN",
-    param_space=config.to_dict(),
+    param_space=config,
     run_config=air.RunConfig(
         stop={"training_iteration": 1},
     ),
@@ -127,6 +148,9 @@ def sample(
 # __sphinx_doc_replay_buffer_advanced_usage_underlying_buffers__begin__
 config = (
     DQNConfig()
+    .api_stack(
+        enable_env_runner_and_connector_v2=False, enable_rl_module_and_learner=False
+    )
     .training(
         replay_buffer_config={
             "type": "MultiAgentReplayBuffer",

doc/source/rllib/doc_code/training.py

@@ -29,23 +29,31 @@
 # __query_action_dist_start__
 # Get a reference to the policy
 import numpy as np
+import torch
+
 from ray.rllib.algorithms.dqn import DQNConfig
 
 algo = (
     DQNConfig()
+    .api_stack(
+        enable_rl_module_and_learner=False, enable_env_runner_and_connector_v2=False
+    )
     .environment("CartPole-v1")
-    .framework("tf2")
     .env_runners(num_env_runners=0)
-    .build()
-)
+    .training(
+        replay_buffer_config={
+            "type": "MultiAgentPrioritizedReplayBuffer",
+        }
+    )
+).build()
 # <ray.rllib.algorithms.ppo.PPO object at 0x7fd020186384>
 
 policy = algo.get_policy()
 # <ray.rllib.policy.eager_tf_policy.PPOTFPolicy_eager object at 0x7fd020165470>
 
 # Run a forward pass to get model output logits. Note that complex observations
 # must be preprocessed as in the above code block.
-logits, _ = policy.model({"obs": np.array([[0.1, 0.2, 0.3, 0.4]])})
+logits, _ = policy.model({"obs": torch.from_numpy(np.array([[0.1, 0.2, 0.3, 0.4]]))})
 # (<tf.Tensor: id=1274, shape=(1, 2), dtype=float32, numpy=...>, [])
 
 # Compute action distribution given logits
@@ -57,14 +65,14 @@
 # Query the distribution for samples, sample logps
 dist.sample()
 # <tf.Tensor: id=661, shape=(1,), dtype=int64, numpy=..>
-dist.logp([1])
+dist.logp(torch.tensor([1]))
 # <tf.Tensor: id=1298, shape=(1,), dtype=float32, numpy=...>
 
 # Get the estimated values for the most recent forward pass
 policy.model.value_function()
 # <tf.Tensor: id=670, shape=(1,), dtype=float32, numpy=...>
 
-policy.model.base_model.summary()
+print(policy.model)
 """
 Model: "model"
 _____________________________________________________________________
@@ -95,23 +103,36 @@
 # __get_q_values_dqn_start__
 # Get a reference to the model through the policy
 import numpy as np
+import torch
+
 from ray.rllib.algorithms.dqn import DQNConfig
 
-algo = DQNConfig().environment("CartPole-v1").framework("tf2").build()
+algo = (
+    DQNConfig()
+    .api_stack(
+        enable_rl_module_and_learner=False, enable_env_runner_and_connector_v2=False
+    )
+    .environment("CartPole-v1")
+    .training(
+        replay_buffer_config={
+            "type": "MultiAgentPrioritizedReplayBuffer",
+        }
+    )
+).build()
 model = algo.get_policy().model
 # <ray.rllib.models.catalog.FullyConnectedNetwork_as_DistributionalQModel ...>
 
 # List of all model variables
-model.variables()
+list(model.parameters())
 
 # Run a forward pass to get base model output. Note that complex observations
 # must be preprocessed. An example of preprocessing is
 # examples/offline_rl/saving_experiences.py
-model_out = model({"obs": np.array([[0.1, 0.2, 0.3, 0.4]])})
+model_out = model({"obs": torch.from_numpy(np.array([[0.1, 0.2, 0.3, 0.4]]))})
 # (<tf.Tensor: id=832, shape=(1, 256), dtype=float32, numpy=...)
 
 # Access the base Keras models (all default models have a base)
-model.base_model.summary()
+print(model)
 """
 Model: "model"
 _______________________________________________________________________
@@ -132,16 +153,16 @@
 """
 
 # Access the Q value model (specific to DQN)
-print(model.get_q_value_distributions(model_out)[0])
+print(model.get_q_value_distributions(model_out[0])[0])
 # tf.Tensor([[ 0.13023682 -0.36805138]], shape=(1, 2), dtype=float32)
 # ^ exact numbers may differ due to randomness
 
-model.q_value_head.summary()
+print(model.advantage_module)
 
 # Access the state value model (specific to DQN)
-print(model.get_state_value(model_out))
+print(model.get_state_value(model_out[0]))
 # tf.Tensor([[0.09381643]], shape=(1, 1), dtype=float32)
 # ^ exact number may differ due to randomness
 
-model.state_value_head.summary()
+print(model.value_module)
 # __get_q_values_dqn_end__