[rllib] Added evaluation script to RLLib (#1295)

python/ray/rllib/eval.py

@@ -0,0 +1,68 @@
+#!/usr/bin/env python
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import gym
+import json
+import ray
+
+from ray.rllib.agent import get_agent_class
+
+
+EXAMPLE_USAGE = """
+example usage:
+    ./eval.py /tmp/ray/checkpoint_dir/checkpoint-0 --run DQN --env CartPole-v0
+"""
+
+
+parser = argparse.ArgumentParser(
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        description="Evaluates a reinforcement learning agent "
+        "given a checkpoint.", epilog=EXAMPLE_USAGE)
+
+parser.add_argument(
+        "checkpoint", type=str, help="Checkpoint from which to evaluate.")
+required_named = parser.add_argument_group("required named arguments")
+required_named.add_argument(
+        "--run", type=str, required=True,
+        help="The algorithm or model to train. This may refer to the name "
+        "of a built-on algorithm (e.g. RLLib's DQN or PPO), or a "
+        "user-defined trainable function or class registered in the "
+        "tune registry.")
+required_named.add_argument(
+        "--env", type=str, help="The gym environment to use.")
+parser.add_argument(
+        "--no-render", default=False, action="store_const", const=True,
+        help="Surpress rendering of the environment.")
+parser.add_argument(
+        "--loop-forever", default=False, action="store_const", const=True,
+        help="Run evaluation of the agent forever.")
+parser.add_argument(
+        "--config", default="{}", type=json.loads,
+        help="Algorithm-specific configuration (e.g. env, hyperparams), ")
+
+if __name__ == "__main__":
+    args = parser.parse_args()
+
+    if not args.env:
+        if not args.config.get("env"):
+            parser.error("the following arguments are required: --env")
+        args.env = args.config.get("env")
+
+    ray.init()
+
+    cls = get_agent_class(args.run)
+    agent = cls(env=args.env)
+    agent.restore(args.checkpoint)
+
+    env = gym.make(args.env)
+    state = env.reset()
+    done = False
+    while args.loop_forever or not done:
+        action = agent.compute_action(state)
+        state, reward, done, _ = env.step(action)
+        if not args.no_render:
+            env.render()