add sac

examples/sac/train_sac_beta.py

@@ -0,0 +1,51 @@
+""""""
+import numpy as np
+
+from openrl.configs.config import create_config_parser
+from openrl.envs.common import make
+from openrl.modules.common import SACNet as Net
+from openrl.runners.common import SACAgent as Agent
+
+
+def train():
+    cfg_parser = create_config_parser()
+    cfg = cfg_parser.parse_args()
+
+    # create environment, set environment parallelism
+    env = make("Pendulum-v1", env_num=9)
+
+    # create the neural network
+    net = Net(env, cfg=cfg)
+    # initialize the trainer
+    agent = Agent(net)
+    # start training, set total number of training steps
+    agent.train(total_time_steps=20000)
+
+    env.close()
+    return agent
+
+
+def evaluation(agent):
+    # begin to test
+    # Create an environment for testing and set the number of environments to interact with to 9. Set rendering mode to group_human.
+    render_mode = None
+    env = make("Pendulum-v1", render_mode=render_mode, env_num=9, asynchronous=True)
+    # 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
+    step = 0
+    while not np.any(done):
+        # Based on environmental observation input, predict next action.
+        action = agent.act(obs, sample=False)  # sample=False in evaluation
+        obs, r, done, info = env.step(action)
+        step += 1
+        if step % 50 == 0:
+            print(f"{step}: reward:{np.mean(r)}")
+    env.close()
+
+
+if __name__ == "__main__":
+    agent = train()
+    evaluation(agent)