[RLlib] DreamerV3: rllib train and README.md fixes. (ray-project#38259

)

Signed-off-by: e428265 <arvind.chandramouli@lmco.com>

rllib/algorithms/dreamerv3/README.md

@@ -62,13 +62,13 @@ in combination with the following scripts and command lines in order to run RLli
 ### Atari100k
 ```shell
 $ cd ray/rllib
-$ rllib train --file ../tuned_examples/dreamerv3/atari_100k.py --env ALE/Pong-v5 
+$ rllib train file tuned_examples/dreamerv3/atari_100k.py --env ALE/Pong-v5 
 ```
 
 ### DeepMind Control Suite (vision)
 ```shell
-$ cd ray/rllib/tests
-$ rllib train --file ../tuned_examples/dreamerv3/dm_control_suite_vision.py --env DMC/cartpole/swingup 
+$ cd ray/rllib
+$ rllib train file tuned_examples/dreamerv3/dm_control_suite_vision.py --env DMC/cartpole/swingup 
 ```
 Other `--env` options for the DM Control Suite would be `--env DMC/hopper/hop`, `--env DMC/walker/walk`, etc..
 Note that you can also switch on WandB logging with the above script via the options

rllib/common.py

@@ -144,13 +144,15 @@ def get_help(key: str) -> str:
     ray_num_nodes="Emulate multiple cluster nodes for debugging.",
     ray_object_store_memory="--object-store-memory to use if starting a new cluster.",
     upload_dir="Optional URI to sync training results to (e.g. s3://bucket).",
-    trace="Whether to attempt to enable tracing for eager mode.",
+    trace="Whether to attempt to enable eager-tracing for framework=tf2.",
     torch="Whether to use PyTorch (instead of tf) as the DL framework. "
     "This argument is deprecated, please use --framework to select 'torch'"
     "as backend.",
-    eager="Whether to attempt to enable TensorFlow eager execution. "
-    "This argument is deprecated, please choose 'tf2' in "
-    "--framework to run select eager mode.",
+    wandb_key="An optional WandB API key for logging all results to your WandB "
+    "account.",
+    wandb_project="An optional project name under which to store the training results.",
+    wandb_run_name="An optional name for the specific run under which to store the "
+    "training results.",
 )
 
 
@@ -247,6 +249,13 @@ class CLIArguments:
     RayObjectStoreMemory = typer.Option(
         None, help=train_help.get("ray_object_store_memory")
     )
+    WandBKey = typer.Option(None, "--wandb-key", help=train_help.get("wandb_key"))
+    WandBProject = typer.Option(
+        None, "--wandb-project", help=eval_help.get("wandb_project")
+    )
+    WandBRunName = typer.Option(
+        None, "--wandb-run-name", help=eval_help.get("wandb_run_name")
+    )
     # __cli_train_end__
 
     # Eval arguments

rllib/train.py

@@ -132,6 +132,8 @@ def file(
     config_file: str = cli.ConfigFile,
     # stopping conditions
     stop: Optional[str] = cli.Stop,
+    # Environment override.
+    env: Optional[str] = cli.Env,
     # Checkpointing
     checkpoint_freq: int = cli.CheckpointFreq,
     checkpoint_at_end: bool = cli.CheckpointAtEnd,
@@ -143,9 +145,9 @@ def file(
     framework: FrameworkEnum = cli.Framework,
     trace: bool = cli.Trace,
     # WandB options.
-    wandb_key: Optional[str] = None,
-    wandb_project: Optional[str] = None,
-    wandb_run_name: Optional[str] = None,
+    wandb_key: Optional[str] = cli.WandBKey,
+    wandb_project: Optional[str] = cli.WandBProject,
+    wandb_run_name: Optional[str] = cli.WandBRunName,
     # Ray cluster options.
     local_mode: bool = cli.LocalMode,
     ray_address: Optional[str] = cli.RayAddress,
@@ -193,6 +195,11 @@ def file(
     experiment = experiments[exp_name]
     algo = experiment["run"]
 
+    # Override the env from the config by the value given on the command line.
+    if env is not None:
+        experiment["env"] = env
+
+    # WandB logging support.
     callbacks = None
     if wandb_key is not None:
         project = wandb_project or (