From 3c31b5a48a35830af2d1cfd633d04bdf05fae06a Mon Sep 17 00:00:00 2001
From: Geo Jolly <geojollyc@gmail.com>
Date: Thu, 7 Dec 2023 15:49:19 +0530
Subject: [PATCH] Remove unwanted test for envpool

---
 examples/envpool/test_model.py | 78 ----------------------------------
 examples/envpool/train_ppo.py  |  1 -
 2 files changed, 79 deletions(-)
 delete mode 100644 examples/envpool/test_model.py

diff --git a/examples/envpool/test_model.py b/examples/envpool/test_model.py
deleted file mode 100644
index c0b4ddfb..00000000
--- a/examples/envpool/test_model.py
+++ /dev/null
@@ -1,78 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-# Copyright 2023 The OpenRL Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     https://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-""""""
-
-# Use OpenRL to load stable-baselines's model for testing
-
-import numpy as np
-import torch
-
-from openrl.configs.config import create_config_parser
-from openrl.envs.common import make
-from openrl.modules.common.ppo_net import PPONet as Net
-from openrl.modules.networks.policy_value_network_sb3 import (
-    PolicyValueNetworkSB3 as PolicyValueNetwork,
-)
-from openrl.runners.common import PPOAgent as Agent
-
-
-def evaluation(local_trained_file_path=None):
-    # begin to test
-
-    cfg_parser = create_config_parser()
-    cfg = cfg_parser.parse_args(["--config", "ppo.yaml"])
-
-    # Create an environment for testing and set the number of environments to interact with to 9. Set rendering mode to group_human.
-    render_mode = "group_human"
-    render_mode = None
-    env = make("CartPole-v1", render_mode=render_mode, env_num=9, asynchronous=True)
-    model_dict = {"model": PolicyValueNetwork}
-    net = Net(
-        env,
-        cfg=cfg,
-        model_dict=model_dict,
-        device="cuda" if torch.cuda.is_available() else "cpu",
-    )
-    # initialize the trainer
-    agent = Agent(
-        net,
-    )
-    if local_trained_file_path is not None:
-        agent.load(local_trained_file_path)
-    # The trained agent sets up the interactive environment it needs.
-    agent.set_env(env)
-    # Initialize the environment and get initial observations and environmental information.
-    obs, info = env.reset()
-    done = False
-
-    total_step = 0
-    total_reward = 0.0
-    while not np.any(done):
-        # Based on environmental observation input, predict next action.
-        action, _ = agent.act(obs, deterministic=True)
-        obs, r, done, info = env.step(action)
-        total_step += 1
-        total_reward += np.mean(r)
-        if total_step % 50 == 0:
-            print(f"{total_step}: reward:{np.mean(r)}")
-    env.close()
-    print("total step:", total_step)
-    print("total reward:", total_reward)
-
-
-if __name__ == "__main__":
-    evaluation()
diff --git a/examples/envpool/train_ppo.py b/examples/envpool/train_ppo.py
index 4120ee4a..49de50f4 100644
--- a/examples/envpool/train_ppo.py
+++ b/examples/envpool/train_ppo.py
@@ -16,7 +16,6 @@
 
 """"""
 import numpy as np
-from test_model import evaluation
 
 from openrl.configs.config import create_config_parser
 from openrl.envs.common import make