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cartpole-v1/README.md

@@ -16,7 +16,7 @@ model-index:
       type: CartPole-v1
     metrics:
     - type: mean_reward
-      value: 0.19 +/- 0.03
+      value: 0.21 +/- 0.03
       name: mean_reward
       verified: false
 ---

cartpole-v1/model.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ee315b44d3dbc05cd7ba1d04fc0b2b0c49605d506c21e34be0e3239656b0aaf9
+oid sha256:803f3cedf787a5521a892e8a7df859011e2fb2bf339cc50898fa529529706594
 size 3264

cartpole-v1/results.json

@@ -1 +1 @@
-{"env_id": "CartPole-v1", "mean_reward": 0.1875143900513649, "n_evaluation_episodes": 10, "eval_datetime": "2024-03-09T00:13:58.880496"}
+{"env_id": "CartPole-v1", "mean_reward": 0.2060810938477516, "n_evaluation_episodes": 10, "eval_datetime": "2024-03-09T00:16:05.690158"}

policy_gradient.py

@@ -27,7 +27,7 @@ env_id = "CartPole-v1"
 env = gym.make(env_id, render_mode="rgb_array")
 
 # Create the evaluation env
-eval_env = gym.make(env_id)
+eval_env = gym.make(env_id, render_mode="rgb_array")
 
 # Get the state space and action space
 s_size = env.observation_space.shape[0]