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+# SLM-Lab v5.1.0
+
+TorchArc YAML benchmarks replace original hardcoded network architectures across all benchmark categories.
+
+- **TorchArc integration**: All algorithms (REINFORCE, SARSA, DQN, DDQN+PER, A2C, PPO, SAC) now use TorchArc YAML-defined networks instead of hardcoded PyTorch modules
+- **Full benchmark validation**: Classic Control, Box2D, MuJoCo (11 envs), and Atari (54 games) re-benchmarked with TorchArc — results match or exceed original scores
+- **SAC Atari**: New SAC Atari benchmarks (48 games) with discrete action support
+- **Pre-commit hooks**: Conventional commit message validation via `.githooks/commit-msg`
+
+---
+
+# SLM-Lab v5.0.0
+
+Modernization release for the current RL ecosystem. Updates SLM-Lab from OpenAI Gym to Gymnasium, adds correct handling of episode termination (the `terminated`/`truncated` fix), and migrates to modern Python tooling.
+
+**TL;DR:** Install with `uv sync`, run with `slm-lab run`. Specs are simpler (no more `body` section or array wrappers). Environment names changed (`CartPole-v1`, `ALE/Pong-v5`, `Hopper-v5`). Code structure preserved for book readers.
+
+> **Book readers:** For exact code from *Foundations of Deep Reinforcement Learning*, use `git checkout v4.1.1`
+
+---
+
+## Why This Release
+
+SLM-Lab was created as an educational framework for deep reinforcement learning, accompanying *Foundations of Deep Reinforcement Learning*. The code prioritizes clarity and correctness—it should help you understand RL algorithms, not just run them.
+
+Since v4, the RL ecosystem changed significantly:
+
+- **OpenAI Gym is deprecated.** The Farama Foundation forked it as [Gymnasium](https://gymnasium.farama.org/), now the standard. Gym's `done` flag conflated two concepts: true termination (agent failed/succeeded) and time-limit truncation. Gymnasium fixes this with separate `terminated` and `truncated` signals—important for correct value estimation (see [below](#the-gymnasium-api-change)).
+
+- **Roboschool is abandoned.** MuJoCo became free in 2022, so roboschool is no longer maintained. Gymnasium includes native MuJoCo bindings.
+
+- **Python tooling modernized.** `conda` + `setup.py` → `uv` + `pyproject.toml`. Python 3.12+, PyTorch 2.8+. [uv](https://docs.astral.sh/uv/) emerged as a fast, reliable Python package manager—no more conda environment headaches.
+
+- **Old dependencies don't build anymore.** The v4 dependency stack (old PyTorch, atari-py, mujoco-py, etc.) won't compile on modern hardware, especially ARM machines (Apple Silicon, AWS Graviton). Many deprecated packages simply don't run. A full rebuild was necessary.
+
+This release updates SLM-Lab to work with modern dependencies while preserving the educational code structure. If you've read the book, the code should still be recognizable.
+
+### Critical: Atari v5 Sticky Actions
+
+**SLM-Lab uses Gymnasium ALE v5 defaults.** v5 default `repeat_action_probability=0.25` (sticky actions) randomly repeats agent actions to simulate console stochasticity, making evaluation harder but more realistic than v4 default 0.0 used by most benchmarks (CleanRL, SB3, RL Zoo). This follows [Machado et al. (2018)](https://arxiv.org/abs/1709.06009) research best practices. See [ALE version history](https://ale.farama.org/environments/#version-history-and-naming-schemes).
+
+### Summary
+
+| v4 | v5 |
+|----|----|
+| `conda activate lab && python run_lab.py` | `slm-lab run` |
+| `CartPole-v0`, `PongNoFrameskip-v4` | `CartPole-v1`, `ALE/Pong-v5` |
+| `RoboschoolHopper-v1` | `Hopper-v5` |
+| `agent: [{...}]`, `env: [{...}]`, `body: {...}` | `agent: {...}`, `env: {...}` |
+| `body.state_dim`, `body.memory` | `agent.state_dim`, `agent.memory` |
+
+---
+
+## Migration from v4
+
+### 1. Install
+
+```bash
+uv sync
+uv tool install --editable .
+```
+
+### 2. Update specs
+
+Remove array brackets and `body` section:
+
+```diff
+ {
+-  "agent": [{ "name": "PPO", ... }],
+-  "env": [{ "name": "CartPole-v0", ... }],
+-  "body": { "product": "outer", "num": 1 },
++  "agent": { "name": "PPO", ... },
++  "env": { "name": "CartPole-v1", ... },
+   "meta": { ... }
+ }
+```
+
+### 3. Update environment names
+
+- Classic control: `v0`/`v1` → current version (`CartPole-v1`, `Pendulum-v1`, `LunarLander-v3`)
+- Atari: `PongNoFrameskip-v4` → `ALE/Pong-v5`
+- Roboschool → MuJoCo: see [Deprecations](#roboschool) for full mapping
+
+### 4. Run
+
+```bash
+slm-lab run spec.json spec_name train
+```
+
+See `slm_lab/spec/benchmark/` for updated reference specs.
+
+---
+
+## The Gymnasium API Change
+
+This matters for understanding the code, not just running it.
+
+### The Problem
+
+Gym's `done` flag was ambiguous—it meant "episode ended" but episodes end for two different reasons:
+
+1. **Terminated:** True end state (CartPole fell, agent died, goal reached)
+2. **Truncated:** Time limit hit (MuJoCo's 1000-step cap)
+
+For value estimation, these need different treatment. Terminated means future returns are zero. Truncated means future returns exist but weren't observed—you should bootstrap from V(s').
+
+### The Fix
+
+Gymnasium separates the signals:
+
+```python
+# Gym
+obs, reward, done, info = env.step(action)
+
+# Gymnasium
+obs, reward, terminated, truncated, info = env.step(action)
+```
+
+All SLM-Lab algorithms now use `terminated` for bootstrapping decisions:
+
+```python
+# Only zero out future returns on TRUE termination
+q_targets = rewards + gamma * (1 - terminateds) * next_q_preds
+```
+
+This is why the code stores `terminateds` and `truncateds` separately in memory—algorithms need `terminated` for correct bootstrapping, `done` for episode boundaries.
+
+This fix particularly matters for time-limited environments like MuJoCo (1000-step limit) where episodes frequently truncate during training. Using `done` instead of `terminated` there significantly hurts learning.
+
+---
+
+## Code Structure Changes
+
+For book readers who want to trace through the code:
+
+### Simplified Agent Design
+
+The `Body` class was removed. Its responsibilities moved to more natural locations:
+
+```python
+# v4
+state_dim = agent.body.state_dim
+memory = agent.body.memory
+env = agent.body.env
+
+# v5
+state_dim = agent.state_dim
+memory = agent.memory
+env = agent.env
+```
+
+Training metrics tracking is now in `MetricsTracker` (what `Body` was renamed to).
+
+### Simplified Specs
+
+Multi-agent configurations were rarely used. Specs are now flat:
+
+```python
+# v4: agent_spec = spec['agent'][0]
+# v5: agent_spec = spec['agent']
+```
+
+### Architecture Preserved
+
+The core design is unchanged:
+
+```
+Session → Agent → Algorithm → Network
+              ↘ Memory
+        → Env
+```
+
+---
+
+## Algorithm Updates
+
+**PPO:** New options for value target handling—`normalize_v_targets`, `symlog_transform` (from DreamerV3), `clip_vloss` (CleanRL-style).
+
+**SAC:** Discrete action support uses exact expectation (Christodoulou 2019). Target entropy auto-calculated.
+
+**Networks:** Optional `layer_norm` for MLP hidden layers. Custom optimizers (Lookahead, RAdam) removed—use native PyTorch `AdamW`.
+
+All algorithms use `terminated` (not `done`) for correct bootstrapping.
+
+---
+
+## Benchmarks
+
+All algorithms validated on Gymnasium. Full results in `docs/BENCHMARKS.md`.
+
+| Category | REINFORCE | SARSA | DQN | DDQN+PER | A2C | PPO | SAC |
+|----------|-----------|-------|-----|----------|-----|-----|-----|
+| Classic Control | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
+| Box2D | — | — | ✅ | ✅ | ⚠️ | ✅ | ✅ |
+| MuJoCo (11 envs) | — | — | — | — | ⚠️ | ✅ All | ✅ All |
+| Atari (54 games) | — | — | — | — | ✅ | ✅ | — |
+
+**Atari benchmarks** use ALE v5 with sticky actions (`repeat_action_probability=0.25`). PPO tested with lambda variants (0.95, 0.85, 0.70) to optimize per-game performance. A2C uses GAE with lambda 0.95.
+
+**Note on scores:** Gymnasium environment versions differ from old Gym—some are harder (CartPole-v1 has stricter termination than v0), some have different reward scales (MuJoCo v5 vs roboschool). Targets reference [CleanRL](https://docs.cleanrl.dev/) and [Stable-Baselines3](https://stable-baselines3.readthedocs.io/) gymnasium benchmarks.
+
+---
+
+## New Features
+
+**Hyperparameter search** now uses Ray Tune + Optuna + ASHA early stopping:
+
+```bash
+slm-lab run spec.json spec_name search    # Run search locally
+```
+
+Add `search_scheduler` to spec for ASHA early termination of poor trials. See `docs/BENCHMARKS.md` for search methodology.
+
+---
+
+## CLI Usage
+
+The CLI uses [Typer](https://typer.tiangolo.com/). Use `--help` on any command for details:
+
+```bash
+slm-lab --help                           # List all commands
+slm-lab run --help                       # Options for run command
+
+# Installation
+uv sync                                  # Install dependencies
+uv tool install --editable .             # Install slm-lab command
+
+# Basic usage
+slm-lab run                              # PPO CartPole (default demo)
+slm-lab run --render                     # With visualization
+slm-lab run spec.json spec_name train    # Train from spec file
+slm-lab run spec.json spec_name dev      # Dev mode (shorter run)
+slm-lab run spec.json spec_name search   # Hyperparameter search
+
+# Variable substitution (for template specs)
+slm-lab run -s env=ALE/Breakout-v5 slm_lab/spec/benchmark/ppo/ppo_atari.json ppo_atari train
+
+# Cloud training (dstack + HuggingFace)
+slm-lab run-remote --gpu spec.json spec_name train   # Launch on cloud GPU
+slm-lab list                                         # List experiments on HuggingFace
+slm-lab pull spec_name                               # Download results locally
+
+# Utilities
+slm-lab run --stop-ray                   # Stop Ray processes
+```
+
+Modes: `dev` (quick test), `train` (full training), `search` (hyperparameter search), `enjoy` (evaluate saved model).
+
+---
+
+## Deprecations
+
+### Multi-Agent / Multi-Environment
+
+The v4 `body` spec section and array wrappers (`agent: [{...}]`) supported multi-agent and multi-environment configurations. These were rarely used and added complexity. v5 simplifies to single-agent single-env, which covers the vast majority of use cases and matches how most RL research is done.
+
+### Unity ML-Agents and VizDoom
+
+These integrations are removed from the core package. Both ecosystems have their own gymnasium-compatible wrappers now:
+- Unity: [gymnasium-unity](https://gymnasium.farama.org/environments/third_party_environments/)
+- VizDoom: [vizdoom gymnasium wrapper](https://gymnasium.farama.org/environments/third_party_environments/)
+
+You can still use these environments with SLM-Lab by installing their wrappers and specifying the environment name in your spec.
+
+### Roboschool
+
+Roboschool is abandoned (MuJoCo became free in 2022). Use gymnasium's native MuJoCo environments instead:
+- `RoboschoolHopper-v1` → `Hopper-v5`
+- `RoboschoolHalfCheetah-v1` → `HalfCheetah-v5`
+- `RoboschoolWalker2d-v1` → `Walker2d-v5`
+- `RoboschoolAnt-v1` → `Ant-v5`
+- `RoboschoolHumanoid-v1` → `Humanoid-v5`