Able to train, but output actions are not executed when predicting the model

**Describe the bug**
When I train the model, I can give the results of each training output, but when I predict, the model does not execute transactions

**To Reproduce**
Steps to reproduce the behavior:
### train code

PPO_model_kwargs = {
"n_steps": 512,
"ent_coef": 0.002,
"learning_rate": 0.00025,
"batch_size": 1024,
"device": "cuda",
}
ppo_model = agent.get_model("ppo", model_kwargs=PPO_model_kwargs, seed=0, tensorboard_log="PPO_VOTE_temp")

train_model = agent.train_model(model=ppo_model, total_timesteps=len(macro_df) * 8000, tb_log_name="PPO_VOTE_temp")

### train output

=================================
day: 45, episode: 8204
begin_total_asset: 100204894.97
end_total_asset: 110930148.45
total_reward: 10725253.48
total_cost: 9998.75
total_trades: 41
Sharpe: 4.197
=================================
------------------------------------------
| time/ | |
| fps | 290 |
| iterations | 719 |
| time_elapsed | 1267 |
| total_timesteps | 368128 |
| train/ | |
| approx_kl | 0.0006842093 |
| clip_fraction | 0 |
| clip_range | 0.2 |
| entropy_loss | -1.24 |
| explained_variance | -0.0117 |
| learning_rate | 0.00025 |
| loss | 4.5e+04 |
| n_updates | 7180 |
| policy_gradient_loss | 0.0012 |
| reward | 11.44137 |
| std | 0.837 |
| value_loss | 9e+04 |
------------------------------------------

### predicting code

e_trade_gym = StockTradingEnv(df=macro_df, num_stock_shares=num_stock_shares, **env_kwargs)
df_account_value, df_actions = DRLAgent.DRL_prediction(
model=train_model,
environment=e_trade_gym,
)
df_actions

### predicting output

day: 45, episode: 2
begin_total_asset: 100189432.53
end_total_asset: 100000000.00
total_reward: -189432.53
total_cost: 0.00
total_trades: 0
Sharpe: -2.366
=================================
hit end!

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