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AssertionError Traceback (most recent call last)
Cell In[30], line 1
----> 1 model = PPO('MlpPolicy', env_wrap, verbose=1, learning_rate=linear_schedule(3e-4), tensorboard_log=LOG_DIR)

File ~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\stable_baselines3\ppo\ppo.py:109, in PPO.init(self, policy, env, learning_rate, n_steps, batch_size, n_epochs, gamma, gae_lambda, clip_range, clip_range_vf, normalize_advantage, ent_coef, vf_coef, max_grad_norm, use_sde, sde_sample_freq, rollout_buffer_class, rollout_buffer_kwargs, target_kl, stats_window_size, tensorboard_log, policy_kwargs, verbose, seed, device, _init_setup_model)
80 def init(
81 self,
82 policy: Union[str, Type[ActorCriticPolicy]],
(...)
107 _init_setup_model: bool = True,
108 ):
--> 109 super().init(
110 policy,
111 env,
112 learning_rate=learning_rate,
113 n_steps=n_steps,
114 gamma=gamma,
115 gae_lambda=gae_lambda,
116 ent_coef=ent_coef,
117 vf_coef=vf_coef,
118 max_grad_norm=max_grad_norm,
119 use_sde=use_sde,
120 sde_sample_freq=sde_sample_freq,
121 rollout_buffer_class=rollout_buffer_class,
...
186 raise ValueError(
187 "Error: the model does not support multiple envs; it requires " "a single vectorized environment."
188 )

AssertionError: The algorithm only supports (<class 'gymnasium.spaces.box.Box'>, <class 'gymnasium.spaces.discrete.Discrete'>, <class 'gymnasium.spaces.multi_discrete.MultiDiscrete'>, <class 'gymnasium.spaces.multi_binary.MultiBinary'>) as action spaces but Discrete(7) was provided
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