Add first draft of jumanji rware wrapper.

og_marl/environments/jumanji_rware.py

@@ -0,0 +1,62 @@
+# Copyright 2023 InstaDeep Ltd. All rights reserved.
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Base wrapper for Jumanji environments."""
+from typing import Any, Dict
+
+import numpy as np
+import jax
+
+from og_marl.environments.base import BaseEnvironment, ResetReturn, StepReturn
+
+
+class JumanjiBase(BaseEnvironment):
+
+    """Environment wrapper for Jumanji environments."""
+
+    def __init__(self) -> None:
+        """Constructor."""
+        self._environment = ...
+        self._num_agents = self._environment.num_agents
+        self.possible_agents = [f"agent_{i}" for i in range(self._num_agents)]
+        self._state = ...  # Jumanji environment state
+
+        self.info_spec: Dict[str, Any] = {}
+
+    def reset(self) -> ResetReturn:
+        """Resets the env."""
+        # Reset the environment
+        self.key, sub_key = jax.random.split(self.key)
+        self._state, timestep = self._environment.reset(sub_key)
+
+        # Infos
+        info = {"state": env_state}
+
+        # Convert observations to OLT format
+        observations = self._convert_observations(observations, False)
+
+        return observations, info
+
+    def step(self, actions: Dict[str, np.ndarray]) -> StepReturn:
+        """Steps in env."""
+        actions = ...  # convert actions
+        # Step the environment
+        self._state, timestep = self._env.step(self._state, actions)
+
+        # Global state
+        env_state = self._create_state_representation(observations)
+
+        # Extra infos
+        info = {"state": env_state}
+
+        return observations, rewards, terminals, truncations, info