chore: release v0.2.0

docs/source/en/user_guide/demo/pendulum.md

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+# Pendulum
+
+Pendulum is a single-joint swing-up and balance task. The goal is to swing the pole up and keep it inverted using one motor torque.
+
+```{video} /_static/videos/pendulum.mp4
+:poster: _static/images/poster/pendulum.jpg
+:nocontrols:
+:autoplay:
+:playsinline:
+:muted:
+:loop:
+:width: 100%
+```
+
+---
+
+## Task Description
+
+A single-link pendulum with one hinge joint is driven by a single motor (configurable gear). The motor’s torque rotates the rod in a plane, enabling swing-up from arbitrary initial angles, inverted balance, and maintenance. Torque is limited by the actuator ctrlrange; by modulating its magnitude and direction, the policy must accumulate energy to swing up and stabilize near the inverted position while damping angular-velocity-induced oscillations.
+
+## Action Space
+
+| Item          | Details                         |
+| ------------- | ------------------------------- |
+| **Type**      | `Box(-1.0, 1.0, (1,), float32)` |
+| **Dimension** | 1                               |
+
+---
+
+## Observation Space
+
+| Item          | Details                         |
+| ------------- | ------------------------------- |
+| **Type**      | `Box(-inf, inf, (3,), float32)` |
+| **Dimension** | 3                               |
+
+Order: `cos(theta), sin(theta), angular velocity`.
+
+---
+
+## Reward Function Design
+
+-   Upright reward: encourages angle near π (inverted)
+-   Energy shaping: target energy near inverted position
+-   Penalties: `ang_vel^2`, `ctrl^2`, `(ctrl - prev_ctrl)^2` to reduce oscillation and aggressive actuation
+
+---
+
+## Initial State
+
+-   Angle randomized in `[-pi, pi]`
+-   Angular velocity small random noise (if configured)
+-   Control history (`prev_ctrl`) reset to zero
+
+## Episode Termination Conditions
+
+-   No fall/angle termination; only NaN check
+-   Episode length limited by `max_episode_seconds`
+
+---
+
+### 1. Environment Preview
+
+```bash
+uv run scripts/view.py --env pendulum
+```
+
+### 2. Start Training
+
+```bash
+# Train with default parameters
+uv run scripts/train.py --env pendulum
+
+# Customize parallel environments
+uv run scripts/train.py --env pendulum --num-envs 1024
+
+# Enable rendering during training
+uv run scripts/train.py --env pendulum --render
+```
+
+### 3. View Training Progress
+
+```bash
+uv run tensorboard --logdir runs/pendulum
+```
+
+### 4. Test Training Results
+
+```bash
+# Auto-discover best policy (recommended)
+uv run scripts/play.py --env pendulum
+
+# Manually specify a policy file
+uv run scripts/play.py --env pendulum --policy runs/pendulum/nn/best_policy.pickle
+```
+
+> **Tip**: Policies are auto-selected from `runs/pendulum/`. You can override with `--policy`.
+
+---
+
+## Configuration Parameters
+
+### Environment Configuration
+
+```python
+@dataclass
+class PendulumEnvCfg(EnvCfg):
+    model_file: str = ".../pendulum.xml"  # MJCF model (gear=5)
+    max_episode_seconds: float = 20.0
+    sim_dt: float = 0.0125
+    ctrl_dt: float = 0.025
+```
+
+### Training Configuration (PPO example)
+
+```python
+@rlcfg("pendulum")
+@dataclass
+class PendulumPPO(PPOCfg):
+    seed: int = 42
+    max_env_steps: int = 10_000_000
+    num_envs: int = 1024
+    learning_rate: float = 3e-4
+    rollouts: int = 32
+    learning_epochs: int = 5
+    mini_batches: int = 4
+    policy_hidden_layer_sizes: tuple[int, ...] = (64, 64)
+    value_hidden_layer_sizes: tuple[int, ...] = (64, 64)
+```
+
+---
+
+## Expected Training Results
+
+1. Pendulum can swing up and stay near inverted
+2. Oscillation around upright is reduced by angular-velocity and control-change penalties