minor

        # Store the format prompt function for later use

        self.format_prompt_func = format_prompt[self.config.prompt_format]

        self.parse_func = parse_function_map[self.config.prompt_format.rstrip("_symbol")]

        self.parse_func = parse_function_map[self.config.prompt_format]

    def reset(self, seed=None):

        """

    min_actions_to_succeed: int = 5

    prompt_format: str = "free_think" 

    # "free_think", "no_think", "grounding", "worldmodeling", "grounding_worldmodeling"

    # "grounding_symbol", "worldmodeling_symbol", "grounding_worldmodeling_symbol"

    # "grounding_symbolic", "worldmodeling_symbolic", "grounding_worldmodeling_symbolic"

    # "grounding_structured", "worldmodeling_structured", "grounding_worldmodeling_structured"

    use_accuracy_reward: bool = False

        "example": "<think><observation>The player is on the above the target</observation><reasoning>I should go down then left to reach the target</reasoning><prediction>The player will reach the target</prediction></think><answer>Down{action_sep}Left</answer>"

    },

    "grounding_symbol": {

    "grounding_symbolic": {

        "format": "<think><observation>...</observation><reasoning>...</reasoning></think><answer>...</answer>",

        "description": "You should first describe the observation as a grid, then your reasoning, and finally your answer.",

        "additional_info": "The observation should be represented as a grid using the symbols: _ Frozen | O Hole | G Goal | P Player | X Player fell into hole | √ Player on goal.",

        "example": "<think><observation>_P__\nG___\n*OO*\n____</observation><reasoning>I should go down then left to reach the target</reasoning></think><answer>Down{action_sep}Left</answer>"

    },

    "worldmodeling_symbol": {

    "worldmodeling_symbolic": {

        "format": "<think><reasoning>...</reasoning><prediction>...</prediction></think>",

        "description": "You should first give your reasoning, then predict the next state, and finally your answer.",

        "additional_info": "The prediction should be represented as a grid using the symbols: _ Frozen | O Hole | G Goal | P Player | X Player fell into hole | √ Player on goal.",

        "example": "<think><reasoning>I can see the target is on my down left, I should go down then left</reasoning><prediction>____\n√___\n*OO*\n____</prediction></think><answer>Down{action_sep}Left</answer>"

    },

    "grounding_worldmodeling_symbol": {

    "grounding_worldmodeling_symbolic": {

        "format": "<think><observation>...</observation><reasoning>...</reasoning><prediction>...</prediction></think>",

        "description": "You should first describe the observation as a grid, then your reasoning, then predict the next state, and finally your answer.",

        "additional_info": "The observation and state should be represented as grids using the symbols: _ Frozen | O Hole | G Goal | P Player | X Player fell into hole | √ Player on goal.",

    while True:

        i += 1

        action = input("Enter action:")

        action = f"<think>Let me try this direction.</think><answer>{action}</answer>"

        #action = f"<think>Let me try this direction.</think><answer>{action}</answer>"

        obs, reward, done, info = env.step(action)

        print(obs["obs_str"])

        # Call the function with add_example=True for system prompt

        self.parse_func = parse_function_map[self.config.prompt_format.rstrip("_symbol")]

        self.parse_func = parse_function_map[self.config.prompt_format]

    def reset(self, seed=None):

        with NoLoggerWarnings():