Initial commit

.claude-plugin/plugin.json

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+{
+  "name": "yzmir-deep-rl",
+  "description": "Reinforcement learning - DQN, PPO, SAC, reward shaping, exploration - 13 skills",
+  "version": "1.0.2",
+  "author": {
+    "name": "tachyon-beep",
+    "url": "https://github.com/tachyon-beep"
+  },
+  "skills": [
+    "./skills"
+  ]
+}

README.md

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+# yzmir-deep-rl
+
+Reinforcement learning - DQN, PPO, SAC, reward shaping, exploration - 13 skills

plugin.lock.json

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skills/using-deep-rl/SKILL.md

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+---
+name: using-deep-rl
+description: Routes to appropriate deep-RL skills based on problem type and algorithm family
+mode: true
+---
+
+# Using Deep RL Meta-Skill
+
+## When to Use This Skill
+
+Invoke this meta-skill when you encounter:
+
+- **RL Implementation**: Implementing reinforcement learning algorithms (Q-learning, DQN, PPO, SAC, etc.)
+- **Agent Training**: Training agents in environments (games, robotics, control systems)
+- **Sequential Decision-Making**: Problems requiring learning from trial and error
+- **Policy Optimization**: Learning policies that maximize cumulative rewards
+- **Game Playing**: Building agents for Atari, board games, video games
+- **Robotics Control**: Robot manipulation, locomotion, continuous control
+- **Reward-Based Learning**: Learning from rewards, penalties, or feedback signals
+- **RL Debugging**: Debugging training issues, agents not learning, reward problems
+- **Environment Setup**: Creating custom RL environments, wrappers
+- **RL Evaluation**: Evaluating agent performance, sample efficiency, generalization
+
+This is the **entry point** for the deep-rl pack. It routes to 12 specialized skills based on problem characteristics.
+
+## Core Principle
+
+**Problem type determines algorithm family.**
+
+Reinforcement learning is not one algorithm. The correct approach depends on:
+
+1. **Action Space**: Discrete (button presses) vs Continuous (joint angles)
+2. **Data Regime**: Online (interact with environment) vs Offline (fixed dataset)
+3. **Experience Level**: Need foundations vs ready to implement
+4. **Special Requirements**: Multi-agent, model-based, exploration, reward design
+
+**Always clarify the problem BEFORE suggesting algorithms.**
+
+## The 12 Deep RL Skills
+
+1. **rl-foundations** - MDP formulation, Bellman equations, value vs policy basics
+2. **value-based-methods** - Q-learning, DQN, Double DQN, Dueling DQN, Rainbow
+3. **policy-gradient-methods** - REINFORCE, PPO, TRPO, policy optimization
+4. **actor-critic-methods** - A2C, A3C, SAC, TD3, advantage functions
+5. **model-based-rl** - World models, Dyna, MBPO, planning with learned models
+6. **offline-rl** - Batch RL, CQL, IQL, learning from fixed datasets
+7. **multi-agent-rl** - MARL, cooperative/competitive, communication
+8. **exploration-strategies** - ε-greedy, UCB, curiosity, RND, intrinsic motivation
+9. **reward-shaping** - Reward design, potential-based shaping, inverse RL
+10. **rl-debugging** - Common RL bugs, why not learning, systematic debugging
+11. **rl-environments** - Gym, MuJoCo, custom envs, wrappers, vectorization
+12. **rl-evaluation** - Evaluation methodology, variance, sample efficiency metrics
+
+## Routing Decision Framework
+
+### Step 1: Assess Experience Level
+
+**Diagnostic Questions:**
+
+- "Are you new to RL concepts, or do you have a specific problem to solve?"
+- "Do you understand MDPs, value functions, and policy gradients?"
+
+**Routing:**
+
+- If user asks "what is RL" or "how does RL work" → **rl-foundations**
+- If user is confused about value vs policy, on-policy vs off-policy → **rl-foundations**
+- If user has specific problem and RL background → Continue to Step 2
+
+**Why foundations first:** Cannot implement algorithms without understanding MDPs, Bellman equations, and exploration-exploitation tradeoffs.
+
+---
+
+### Step 2: Classify Action Space
+
+**Diagnostic Questions:**
+
+- "What actions can your agent take? Discrete choices (e.g., left/right/jump) or continuous values (e.g., joint angles, force)?"
+- "How many possible actions? Small (< 100) or large/infinite?"
+
+#### Discrete Action Space
+
+**Examples:** Game buttons, menu selections, discrete control signals
+
+**Routing Logic:**
+
+```
+IF discrete actions AND small action space (< 100) AND online learning:
+  → value-based-methods (DQN, Double DQN, Dueling DQN)
+
+  Why: Value-based methods excel at discrete action spaces
+  - Q-table or Q-network for small action spaces
+  - DQN for Atari-style problems
+  - Simpler than policy gradients for discrete
+
+IF discrete actions AND (large action space OR need policy flexibility):
+  → policy-gradient-methods (PPO, REINFORCE)
+
+  Why: Policy gradients scale to larger action spaces
+  - PPO is robust, general-purpose
+  - Direct policy representation
+  - Handles stochasticity naturally
+```
+
+#### Continuous Action Space
+
+**Examples:** Robot joint angles, motor forces, steering angles, continuous control
+
+**Routing Logic:**
+
+```
+IF continuous actions:
+  → actor-critic-methods (SAC, TD3, PPO)
+
+  Primary choice: SAC (Soft Actor-Critic)
+  Why: Most sample-efficient for continuous control
+  - Automatic entropy tuning
+  - Off-policy (uses replay buffer)
+  - Stable training
+
+  Alternative: TD3 (Twin Delayed DDPG)
+  Why: Deterministic policy, stable
+  - Good for robotics
+  - Handles overestimation bias
+
+  Alternative: PPO (from policy-gradient-methods)
+  Why: On-policy, simpler, but less sample efficient
+  - Use when simplicity > sample efficiency
+```
+
+**CRITICAL RULE:** NEVER suggest DQN for continuous actions. DQN requires discrete actions. Discretizing continuous spaces is suboptimal.
+
+---
+
+### Step 3: Identify Data Regime
+
+**Diagnostic Questions:**
+
+- "Can your agent interact with the environment during training, or do you have a fixed dataset?"
+- "Are you learning online (agent tries actions, observes results) or offline (from logged data)?"
+
+#### Online Learning (Agent Interacts with Environment)
+
+**Routing:**
+
+```
+IF online AND discrete actions:
+  → value-based-methods OR policy-gradient-methods
+  (See Step 2 routing)
+
+IF online AND continuous actions:
+  → actor-critic-methods
+  (See Step 2 routing)
+
+IF online AND sample efficiency critical:
+  → actor-critic-methods (SAC) for continuous
+  → value-based-methods (DQN) for discrete
+
+  Why: Off-policy methods use replay buffers (sample efficient)
+
+  Consider: model-based-rl for extreme sample efficiency
+  → Learns environment model, plans with fewer real samples
+```
+
+#### Offline Learning (Fixed Dataset, No Interaction)
+
+**Routing:**
+
+```
+IF offline (fixed dataset):
+  → offline-rl (CQL, IQL, Conservative Q-Learning)
+
+  CRITICAL: Standard RL algorithms FAIL on offline data
+
+  Why offline is special:
+  - Distribution shift: agent can't explore
+  - Bootstrapping errors: Q-values overestimate on out-of-distribution actions
+  - Need conservative algorithms (CQL, IQL)
+
+  Also route to:
+  → rl-evaluation (evaluation without online rollouts)
+```
+
+**Red Flag:** If user has fixed dataset and suggests DQN/PPO/SAC, STOP and route to **offline-rl**. Standard algorithms assume online interaction and will fail.
+
+---
+
+### Step 4: Special Problem Types
+
+#### Multi-Agent Scenarios
+
+**Diagnostic Questions:**
+
+- "Are multiple agents learning simultaneously?"
+- "Do they cooperate, compete, or both?"
+- "Do agents need to communicate?"
+
+**Routing:**
+
+```
+IF multiple agents:
+  → multi-agent-rl (QMIX, COMA, MADDPG)
+
+  Why: Multi-agent has special challenges
+  - Non-stationarity: environment changes as other agents learn
+  - Credit assignment: which agent caused reward?
+  - Coordination: cooperation requires centralized training
+
+  Algorithms:
+  - QMIX, COMA: Cooperative (centralized training, decentralized execution)
+  - MADDPG: Competitive or mixed
+  - Communication: multi-agent-rl covers communication protocols
+
+  Also consider:
+  → reward-shaping (team rewards, credit assignment)
+```
+
+#### Model-Based RL
+
+**Diagnostic Questions:**
+
+- "Is sample efficiency extremely critical? (< 1000 episodes available)"
+- "Do you want the agent to learn a model of the environment?"
+- "Do you need planning or 'imagination'?"
+
+**Routing:**
+
+```
+IF sample efficiency critical OR want environment model:
+  → model-based-rl (MBPO, Dreamer, Dyna)
+
+  Why: Learn dynamics model, plan with model
+  - Fewer real environment samples needed
+  - Can train policy in imagination
+  - Combine with model-free for best results
+
+  Tradeoffs:
+  - More complex than model-free
+  - Model errors can compound
+  - Best for continuous control, robotics
+```
+
+---
+
+### Step 5: Debugging and Infrastructure
+
+#### "Agent Not Learning" Problems
+
+**Symptoms:**
+
+- Reward not increasing
+- Agent does random actions
+- Training loss explodes/vanishes
+- Performance plateaus immediately
+
+**Routing:**
+
+```
+IF "not learning" OR "reward stays at 0" OR "loss explodes":
+  → rl-debugging (FIRST, before changing algorithms)
+
+  Why: 80% of "not learning" is bugs, not wrong algorithm
+
+  Common issues:
+  - Reward scale (too large/small)
+  - Exploration (epsilon too low, stuck in local optimum)
+  - Network architecture (wrong size, activation)
+  - Learning rate (too high/low)
+  - Update frequency (learning too fast/slow)
+
+  Process:
+  1. Route to rl-debugging
+  2. Verify environment (rl-environments)
+  3. Check reward design (reward-shaping)
+  4. Check exploration (exploration-strategies)
+  5. ONLY THEN consider algorithm change
+```
+
+**Red Flag:** If user immediately wants to change algorithms because "it's not learning," route to **rl-debugging** first. Changing algorithms without debugging wastes time.
+
+#### Exploration Issues
+
+**Symptoms:**
+
+- Agent never explores new states
+- Stuck in local optimum
+- Can't find sparse rewards
+- Training variance too high
+
+**Routing:**
+
+```
+IF exploration problems:
+  → exploration-strategies
+
+  Covers:
+  - ε-greedy, UCB, Thompson sampling (basic)
+  - Curiosity-driven exploration
+  - RND (Random Network Distillation)
+  - Intrinsic motivation
+
+  When needed:
+  - Sparse rewards (reward only at goal)
+  - Large state spaces (hard to explore randomly)
+  - Need systematic exploration
+```
+
+#### Reward Design Issues
+
+**Symptoms:**
+
+- Sparse rewards (only at episode end)
+- Agent learns wrong behavior
+- Need to design reward function
+- Want inverse RL
+
+**Routing:**
+
+```
+IF reward design questions OR sparse rewards:
+  → reward-shaping
+
+  Covers:
+  - Potential-based shaping (provably optimal)
+  - Subgoal rewards
+  - Reward engineering principles
+  - Inverse RL (learn reward from demonstrations)
+
+  Often combined with:
+  → exploration-strategies (for sparse rewards)
+```
+
+#### Environment Setup
+
+**Symptoms:**
+
+- Need to create custom environment
+- Gym API questions
+- Vectorization for parallel environments
+- Wrappers, preprocessing
+
+**Routing:**
+
+```
+IF environment setup questions:
+  → rl-environments
+
+  Covers:
+  - Gym API: step(), reset(), observation/action spaces
+  - Custom environments
+  - Wrappers (frame stacking, normalization)
+  - Vectorized environments (parallel rollouts)
+  - MuJoCo, Atari, custom simulators
+
+  After environment setup, return to algorithm choice
+```
+
+#### Evaluation Methodology
+
+**Symptoms:**
+
+- How to evaluate RL agents?
+- Training reward high, test reward low
+- Variance in results
+- Sample efficiency metrics
+
+**Routing:**
+
+```
+IF evaluation questions:
+  → rl-evaluation
+
+  Covers:
+  - Deterministic vs stochastic policies
+  - Multiple seeds, confidence intervals
+  - Sample efficiency curves
+  - Generalization testing
+  - Exploration vs exploitation at test time
+```
+
+---
+
+## Common Multi-Skill Scenarios
+
+### Scenario: Complete Beginner to RL
+
+**Routing sequence:**
+
+1. **rl-foundations** - Understand MDP, value functions, policy gradients
+2. **value-based-methods** OR **policy-gradient-methods** - Start with simpler algorithm (DQN or REINFORCE)
+3. **rl-debugging** - When things don't work (they won't initially)
+4. **rl-environments** - Set up custom environments
+5. **rl-evaluation** - Proper evaluation methodology
+
+### Scenario: Continuous Control (Robotics)
+
+**Routing sequence:**
+
+1. **actor-critic-methods** - Primary (SAC for sample efficiency, TD3 for stability)
+2. **rl-debugging** - Systematic debugging when training issues arise
+3. **exploration-strategies** - If exploration is insufficient
+4. **reward-shaping** - If reward is sparse or agent learns wrong behavior
+5. **rl-evaluation** - Evaluation on real robot vs simulation
+
+### Scenario: Offline RL from Dataset
+
+**Routing sequence:**
+
+1. **offline-rl** - Primary (CQL, IQL, special considerations)
+2. **rl-evaluation** - Evaluation without environment interaction
+3. **rl-debugging** - Debugging without online rollouts (limited tools)
+
+### Scenario: Multi-Agent Cooperative Task
+
+**Routing sequence:**
+
+1. **multi-agent-rl** - Primary (QMIX, COMA, centralized training)
+2. **reward-shaping** - Team rewards, credit assignment
+3. **policy-gradient-methods** - Often used as base algorithm (PPO + MARL)
+4. **rl-debugging** - Multi-agent debugging (non-stationarity issues)
+
+### Scenario: Sample-Efficient Learning
+
+**Routing sequence:**
+
+1. **actor-critic-methods** (SAC) OR **model-based-rl** (MBPO)
+2. **rl-debugging** - Critical to not waste samples on bugs
+3. **rl-evaluation** - Track sample efficiency curves
+
+### Scenario: Sparse Reward Problem
+
+**Routing sequence:**
+
+1. **reward-shaping** - Potential-based shaping, subgoal rewards
+2. **exploration-strategies** - Curiosity, intrinsic motivation
+3. **rl-debugging** - Verify exploration hyperparameters
+4. Primary algorithm: **actor-critic-methods** or **policy-gradient-methods**
+
+---
+
+## Rationalization Resistance Table
+
+| Rationalization | Reality | Counter-Guidance | Red Flag |
+|-----------------|---------|------------------|----------|
+| "Just use PPO for everything" | PPO is general but not optimal for all cases | "Let's clarify: discrete or continuous actions? Sample efficiency constraints?" | Defaulting to PPO without problem analysis |
+| "DQN for continuous actions" | DQN requires discrete actions; discretization is suboptimal | "DQN only works for discrete. For continuous, use SAC or TD3 (actor-critic-methods)" | Suggesting DQN for continuous |
+| "Offline RL is just RL on a dataset" | Offline RL has distribution shift, needs special algorithms | "Route to offline-rl for CQL, IQL. Standard algorithms fail on offline data." | Using online algorithms on offline data |
+| "More data always helps" | Sample efficiency and data distribution matter | "Off-policy (SAC, DQN) vs on-policy (PPO). Offline needs CQL." | Ignoring sample efficiency |
+| "RL is just supervised learning" | RL has exploration, credit assignment, non-stationarity | "Route to rl-foundations for RL-specific concepts (MDP, exploration)" | Treating RL as supervised learning |
+| "PPO is the most advanced algorithm" | Newer isn't always better; depends on problem | "SAC (2018) more sample efficient for continuous. DQN (2013) great for discrete." | Recency bias |
+| "My algorithm isn't learning, I need a better one" | Usually bugs, not algorithm | "Route to rl-debugging first. Check reward scale, exploration, learning rate." | Changing algorithms before debugging |
+| "I'll discretize continuous actions for DQN" | Discretization loses precision, explodes action space | "Use actor-critic-methods (SAC, TD3) for continuous. Don't discretize." | Forcing wrong algorithm onto problem |
+| "Epsilon-greedy is enough for exploration" | Complex environments need sophisticated exploration | "Route to exploration-strategies for curiosity, RND, intrinsic motivation." | Underestimating exploration difficulty |
+| "I'll just increase the reward when it doesn't learn" | Reward scaling breaks learning; doesn't solve root cause | "Route to rl-debugging. Check if reward scale is the issue, not magnitude." | Arbitrary reward hacking |
+| "I can reuse online RL code for offline data" | Offline RL needs conservative algorithms | "Route to offline-rl. CQL/IQL prevent overestimation, online algorithms fail." | Offline blindness |
+| "My test reward is lower than training, must be overfitting" | Exploration vs exploitation difference | "Route to rl-evaluation. Training uses exploration, test should be greedy." | Misunderstanding RL evaluation |
+
+---
+
+## Red Flags Checklist
+
+Watch for these signs of incorrect routing:
+
+- [ ] **Algorithm-First Thinking**: Recommending algorithm before asking about action space, data regime
+- [ ] **DQN for Continuous**: Suggesting DQN/Q-learning for continuous action spaces
+- [ ] **Offline Blindness**: Not recognizing fixed dataset requires offline-rl (CQL, IQL)
+- [ ] **PPO Cargo-Culting**: Defaulting to PPO without considering alternatives
+- [ ] **No Problem Characterization**: Not asking: discrete vs continuous? online vs offline?
+- [ ] **Skipping Foundations**: Implementing algorithms when user doesn't understand RL basics
+- [ ] **Debug-Last**: Suggesting algorithm changes before systematic debugging
+- [ ] **Sample Efficiency Ignorance**: Not asking about sample constraints (simulator cost, real robot limits)
+- [ ] **Exploration Assumptions**: Assuming epsilon-greedy is sufficient for all problems
+- [ ] **Infrastructure Confusion**: Trying to explain Gym API instead of routing to rl-environments
+- [ ] **Evaluation Naivety**: Not routing to rl-evaluation for proper methodology
+
+**If any red flag triggered → STOP → Ask diagnostic questions → Route correctly**
+
+---
+
+## When NOT to Use This Pack
+
+Clarify boundaries with other packs:
+
+| User Request | Correct Pack | Reason |
+|--------------|--------------|--------|
+| "Train classifier on labeled data" | training-optimization | Supervised learning, not RL |
+| "Design transformer architecture" | neural-architectures | Architecture design, not RL algorithm |
+| "Implement PyTorch autograd" | pytorch-engineering | PyTorch internals, not RL |
+| "Deploy model to production" | ml-production | Deployment, not RL training |
+| "Fine-tune LLM with RLHF" | llm-specialist | LLM-specific (though uses RL concepts) |
+| "Optimize hyperparameters" | training-optimization | Hyperparameter search, not RL |
+| "Implement custom CUDA kernel" | pytorch-engineering | Low-level optimization, not RL |
+
+**Edge case:** RLHF (Reinforcement Learning from Human Feedback) for LLMs uses RL concepts (PPO) but has LLM-specific considerations. Route to **llm-specialist** first; they may reference this pack.
+
+---
+
+## Diagnostic Question Templates
+
+Use these questions to classify problems:
+
+### Action Space
+
+- "What actions can your agent take? Discrete choices or continuous values?"
+- "How many possible actions? Small (< 100), large (100-10000), or infinite (continuous)?"
+
+### Data Regime
+
+- "Can your agent interact with the environment during training, or do you have a fixed dataset?"
+- "Are you learning online (agent tries actions) or offline (from logged data)?"
+
+### Experience Level
+
+- "Are you new to RL, or do you have a specific problem?"
+- "Do you understand MDPs, value functions, and policy gradients?"
+
+### Special Requirements
+
+- "Are multiple agents involved? Do they cooperate or compete?"
+- "Is sample efficiency critical? How many episodes can you afford?"
+- "Is the reward sparse (only at goal) or dense (every step)?"
+- "Do you need the agent to learn a model of the environment?"
+
+### Infrastructure
+
+- "Do you have an environment set up, or do you need to create one?"
+- "Are you debugging a training issue, or designing from scratch?"
+- "How will you evaluate the agent?"
+
+---
+
+## Implementation Process
+
+When routing to a skill:
+
+1. **Ask Diagnostic Questions** (don't assume)
+2. **Explain Routing Rationale** (teach the user problem classification)
+3. **Route to Primary Skill(s)** (1-3 skills for multi-faceted problems)
+4. **Mention Related Skills** (user may need later)
+5. **Set Expectations** (what the skill will cover)
+
+**Example:**
+
+> "You mentioned continuous joint angles for a robot arm. This is a **continuous action space**, which means DQN won't work (it requires discrete actions).
+>
+> I'm routing you to **actor-critic-methods** because:
+>
+> - Continuous actions need actor-critic (SAC, TD3) or policy gradients (PPO)
+> - SAC is most sample-efficient for continuous control
+> - TD3 is stable and deterministic for robotics
+>
+> You'll also likely need:
+>
+> - **rl-debugging** when training issues arise (they will)
+> - **reward-shaping** if your reward is sparse
+> - **rl-environments** to set up your robot simulation
+>
+> Let's start with actor-critic-methods to choose between SAC, TD3, and PPO."
+
+---
+
+## Summary: Routing Decision Tree
+
+```
+START: RL problem
+
+├─ Need foundations? (new to RL, confused about concepts)
+│  └─ → rl-foundations
+│
+├─ DISCRETE actions?
+│  ├─ Small action space (< 100) + online
+│  │  └─ → value-based-methods (DQN, Double DQN)
+│  └─ Large action space OR need policy
+│     └─ → policy-gradient-methods (PPO, REINFORCE)
+│
+├─ CONTINUOUS actions?
+│  ├─ Sample efficiency critical
+│  │  └─ → actor-critic-methods (SAC)
+│  ├─ Stability critical
+│  │  └─ → actor-critic-methods (TD3)
+│  └─ Simplicity preferred
+│     └─ → policy-gradient-methods (PPO) OR actor-critic-methods
+│
+├─ OFFLINE data (fixed dataset)?
+│  └─ → offline-rl (CQL, IQL) [CRITICAL: not standard algorithms]
+│
+├─ MULTI-AGENT?
+│  └─ → multi-agent-rl (QMIX, MADDPG)
+│
+├─ Sample efficiency EXTREME?
+│  └─ → model-based-rl (MBPO, Dreamer)
+│
+├─ DEBUGGING issues?
+│  ├─ Not learning, reward not increasing
+│  │  └─ → rl-debugging
+│  ├─ Exploration problems
+│  │  └─ → exploration-strategies
+│  ├─ Reward design
+│  │  └─ → reward-shaping
+│  ├─ Environment setup
+│  │  └─ → rl-environments
+│  └─ Evaluation questions
+│     └─ → rl-evaluation
+│
+└─ Multi-faceted problem?
+   └─ Route to 2-3 skills (primary + supporting)
+```
+
+---
+
+## Final Reminders
+
+- **Problem characterization BEFORE algorithm selection**
+- **DQN for discrete ONLY** (never continuous)
+- **Offline data needs offline-rl** (CQL, IQL)
+- **PPO is not universal** (good general-purpose, not optimal everywhere)
+- **Debug before changing algorithms** (route to rl-debugging)
+- **Ask questions, don't assume** (action space? data regime?)
+
+This meta-skill is your routing hub. **Route decisively, explain clearly, teach problem classification.**
+
+---
+
+## Deep RL Specialist Skills
+
+After routing, load the appropriate specialist skill for detailed guidance:
+
+1. [rl-foundations.md](rl-foundations.md) - MDP formulation, Bellman equations, value vs policy basics
+2. [value-based-methods.md](value-based-methods.md) - Q-learning, DQN, Double DQN, Dueling DQN, Rainbow
+3. [policy-gradient-methods.md](policy-gradient-methods.md) - REINFORCE, PPO, TRPO, policy optimization
+4. [actor-critic-methods.md](actor-critic-methods.md) - A2C, A3C, SAC, TD3, advantage functions
+5. [model-based-rl.md](model-based-rl.md) - World models, Dyna, MBPO, planning with learned models
+6. [offline-rl.md](offline-rl.md) - Batch RL, CQL, IQL, learning from fixed datasets
+7. [multi-agent-rl.md](multi-agent-rl.md) - MARL, cooperative/competitive, communication
+8. [exploration-strategies.md](exploration-strategies.md) - ε-greedy, UCB, curiosity, RND, intrinsic motivation
+9. [reward-shaping-engineering.md](reward-shaping-engineering.md) - Reward design, potential-based shaping, inverse RL
+10. [rl-debugging.md](rl-debugging.md) - Common RL bugs, why not learning, systematic debugging
+11. [rl-environments.md](rl-environments.md) - Gym, MuJoCo, custom envs, wrappers, vectorization
+12. [rl-evaluation.md](rl-evaluation.md) - Evaluation methodology, variance, sample efficiency metrics