[rllib] Adds agent name & env id to default logdir prefix (#2859)

* Added agent name & env id to default logdir prefix

* Revert "Added agent name & env id to default logdir prefix"

This reverts commit 07cfdf80d2537da3c67dd4f553c5f3e43671cc7d.

* Added default logger creator with informative prefix to Agent

* Updated import order & improved str cat

* Update agent.py

python/ray/rllib/agents/agent.py

@@ -7,6 +7,8 @@ import json
 import numpy as np
 import os
 import pickle
+import tempfile
+from datetime import datetime
 
 import tensorflow as tf
 from ray.rllib.evaluation.policy_evaluator import PolicyEvaluator
@@ -14,6 +16,8 @@ from ray.rllib.optimizers.policy_optimizer import PolicyOptimizer
 from ray.rllib.utils import FilterManager, deep_update, merge_dicts
 from ray.tune.registry import ENV_CREATOR, _global_registry
 from ray.tune.trainable import Trainable
+from ray.tune.logger import UnifiedLogger
+from ray.tune.result import DEFAULT_RESULTS_DIR
 
 COMMON_CONFIG = {
     # Discount factor of the MDP
@@ -190,6 +194,24 @@ class Agent(Trainable):
 
         # Agents allow env ids to be passed directly to the constructor.
         self._env_id = env or config.get("env")
+
+        # Create a default logger creator if no logger_creator is specified
+        if logger_creator is None:
+            timestr = datetime.today().strftime("%Y-%m-%d_%H-%M-%S")
+            logdir_prefix = '_'.join([self._agent_name, self._env_id, timestr])
+
+            def default_logger_creator(config):
+                """Creates a Unified logger with a default logdir prefix
+                containing the agent name and the env id
+                """
+                if not os.path.exists(DEFAULT_RESULTS_DIR):
+                    os.makedirs(DEFAULT_RESULTS_DIR)
+                logdir = tempfile.mkdtemp(
+                    prefix=logdir_prefix, dir=DEFAULT_RESULTS_DIR)
+                return UnifiedLogger(config, logdir, None)
+
+            logger_creator = default_logger_creator
+
         Trainable.__init__(self, config, logger_creator)
 
     def train(self):