&`i}ddlmZeGddeZeGddeZeGddeZeGdd eZd Zd Zd Z d Z dZ dZ dS)) PublicAPIceZdZdZdS)UnsupportedSpaceExceptionz5Error for an unsupported action or observation space.N__name__ __module__ __qualname____doc__i/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/ray/rllib/utils/error.pyrrs??Dr rceZdZdZdS)EnvErrorz@Error if we encounter an error during RL environment validation.Nrr r r rr sJJDr rceZdZdZdS)MultiAgentEnvErrorzHError if we encounter an error during MultiAgentEnv stepping/validation.Nrr r r rrsRRDr rceZdZdZdS)NotSerializablez>Error if we encounter objects that can't be serialized by ray.Nrr r r rrsHHDr raFound {} GPUs on your machine (GPU devices found: {})! If your machine does not have any GPUs, you should set the config keys `num_gpus_per_learner` and `num_gpus_per_env_runner` to 0. They may be set to 1 by default for your particular RL algorithm.aThe env string you provided ('{}') is: a) Not a supported or an installed environment. b) Not a tune-registered environment creator. c) Not a valid env class string. Try one of the following: a) For Atari support: `pip install gymnasium[atari]` and prefix the environment name with `ale_py:`, for example, `"ale_py:ALE/Pong-v5"`. b) To register your custom env, do `from ray import tune; tune.register_env('[name]', lambda cfg: [return env obj from here using cfg])`. Then in your config, do `config.environment(env='[name]'). c) Make sure you provide a fully qualified classpath, e.g.: `ray.rllib.examples.envs.classes.repeat_after_me_env.RepeatAfterMeEnv` aYour environment ({}) does not abide to the new gymnasium-style API! From Ray 2.3 on, RLlib only supports the new (gym>=0.26 or gymnasium) Env APIs. {} Learn more about the most important changes here: https://github.com/openai/gym and here: https://github.com/Farama-Foundation/Gymnasium In order to fix this problem, do the following: 1) Run `pip install gymnasium` on your command line. 2) Change all your import statements in your code from `import gym` -> `import gymnasium as gym` OR `from gym.spaces import Discrete` -> `from gymnasium.spaces import Discrete` For your custom (single agent) gym.Env classes: 3.1) Either wrap your old Env class via the provided `from gymnasium.wrappers import EnvCompatibility` wrapper class. 3.2) Alternatively to 3.1: - Change your `reset()` method to have the call signature 'def reset(self, *, seed=None, options=None)' - Return an additional info dict (empty dict should be fine) from your `reset()` method. - Return an additional `truncated` flag from your `step()` method (between `done` and `info`). This flag should indicate, whether the episode was terminated prematurely due to some time constraint or other kind of horizon setting. For your custom RLlib `MultiAgentEnv` classes: 4.1) Either wrap your old MultiAgentEnv via the provided `from ray.rllib.env.wrappers.multi_agent_env_compatibility import MultiAgentEnvCompatibility` wrapper class. 4.2) Alternatively to 4.1: - Change your `reset()` method to have the call signature 'def reset(self, *, seed=None, options=None)' - Return an additional per-agent info dict (empty dict should be fine) from your `reset()` method. - Rename `dones` into `terminateds` and only set this to True, if the episode is really done (as opposed to has been terminated prematurely due to some horizon/time-limit setting). - Return an additional `truncateds` per-agent dictionary flag from your `step()` method, including the `__all__` key (100% analogous to your `dones/terminateds` per-agent dict). Return this new `truncateds` dict between `dones/terminateds` and `infos`. This flag should indicate, whether the episode (for some agent or all agents) was terminated prematurely due to some time constraint or other kind of horizon setting. a8Could not save keras model under self[TfPolicy].model.base_model! This is either due to .. a) .. this Policy's ModelV2 not having any `base_model` (tf.keras.Model) property b) .. the ModelV2's `base_model` not being used by the Algorithm and thus its variables not being properly initialized. zCould not save torch model under self[TorchPolicy].model! This is most likely due to the fact that you are using an Algorithm that uses a Catalog-generated TorchModelV2 subclass, which is torch.save() cannot pickle. a To change the config for `tune.Tuner().fit()` in a script: Modify the python dict passed to `tune.Tuner(param_space=[...]).fit()`. To change the config for an RLlib Algorithm instance: Modify the python dict passed to the Algorithm's constructor, e.g. `PPO(config=[...])`. N) ray.rllib.utils.annotationsr ExceptionrrrrERR_MSG_NO_GPUSERR_MSG_INVALID_ENV_DESCRIPTORERR_MSG_OLD_GYM_API)ERR_MSG_TF_POLICY_CANNOT_SAVE_KERAS_MODEL&ERR_MSG_TORCH_POLICY_CANNOT_SAVE_MODELHOWTO_CHANGE_CONFIGr r r rs%111111                y                    i    6 "+\-)*&r