Remove envtorch dir nesting and add base env code

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "envtorch"
-version = "0.0.0"
+version = "0.1.0"
 requires-python = ">=3.8"
 dependencies = [
     "torch>=1.9.0",

src/core/base.py

@@ -0,0 +1,45 @@
+# Abstract base classes for EnvTorch
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from typing import Generic, TypeVar
+
+from .types import StepResult
+
+# Generic type variables
+ActT = TypeVar("ActT")  # Type for the action sent to the environment
+ObsT = TypeVar("ObsT")  # Type for the observation returned by the environment
+
+
+class BaseEnv(ABC, Generic[ActT, ObsT]):
+    """
+    Abstract base class for all environments.
+
+    Each environment must implement:
+      - reset(): to initialize or reinitialize environment state
+      - step(action): to execute an action and return results
+      - close(): to release resources (containers, sessions, etc.)
+    """
+
+    @abstractmethod
+    def reset(self) -> ObsT:
+        """
+        Resets the environment to its initial state.
+
+        Returns:
+            ObsT: The initial observation after resetting.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def step(self, action: ActT) -> StepResult[ObsT]:
+        """
+        Performs one logical step in the environment.
+
+        Args:
+            action (ActT): The action to perform in the environment.
+
+        Returns:
+            StepResult[ObsT]: The resulting observation, reward, done flag, and info.
+        """
+        raise NotImplementedError

src/core/types.py

@@ -0,0 +1,24 @@
+# Type definitions for EnvTorch
+from dataclasses import dataclass
+from typing import Any, Generic, Optional, TypeVar
+
+# Generic type for observations
+ObsT = TypeVar("ObsT")  # TypeVar for typehinting in IDEs
+
+
+@dataclass
+class StepResult(Generic[ObsT]):
+    """
+    Represents the result of one environment step.
+
+    Attributes:
+        observation: The environment's observation after the action.
+        reward: Scalar reward for this step (optional).
+        done: Whether the episode is finished.
+        info: Additional metadata (e.g. debug info, latency, etc.).
+    """
+
+    observation: ObsT
+    reward: Optional[float] = None
+    done: bool = False
+    info: Optional[dict[str, Any]] = None

src/envs/__init__.py

@@ -0,0 +1 @@
+# Environment implementations like CodingEnv here