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Robotics Glossary

Reinforcement Learning

Deep Dive

Reinforcement Learning (RL) is a machine learning paradigm where an agent learns to make decisions by interacting with an environment and receiving rewards. Unlike supervised learning, the agent isn’t told the correct action — it discovers good behavior through trial and error.

The RL Framework

         ┌───────────────────────────────┐
         │         Environment           │
         │   (Robot + World Simulation)  │
         └──────────┬──────────┬─────────┘
                    │          │
              State │          │ Reward
                    ▼          ▼
         ┌───────────────────────────────┐
         │            Agent              │
         │    (Neural Network Policy)    │
         └──────────────┬────────────────┘
                        │
                   Action
                        ▼
                   Environment

Key components:

  • State (s): What the agent observes (sensor readings, joint positions)
  • Action (a): What the agent can do (motor commands, gripper open/close)
  • Reward (r): Scalar feedback signal (positive = good, negative = bad)
  • Policy (π): Mapping from states to actions (what we’re learning)

Why RL for Robotics?

RL shines when:

  • Optimal behavior is unknown — Can’t manually program dexterous manipulation
  • Environment is complex — Physics interactions are hard to model exactly
  • Adaptation is needed — Robot should improve with experience

Classic successes:

  • Quadruped locomotion (ANYmal, Spot)
  • Dexterous manipulation (OpenAI Rubik’s cube)
  • Drone racing (beating human champions)

Core Algorithms

Value-Based Methods

Learn the value of being in a state (or taking an action).

Q-Learning / DQN:

Q(s, a) = expected total reward from taking action a in state s

Pick action with highest Q-value:

action = argmax(Q(state, all_actions))

Limitations: Only works with discrete actions.

Policy Gradient Methods

Directly learn the policy — a probability distribution over actions.

REINFORCE:

∇J(θ) = E[∇log π(a|s) · R]

Increase probability of actions that led to high rewards.

Actor-Critic:

  • Actor: Policy network (what action to take)
  • Critic: Value network (how good is this state)
AlgorithmTypeBest For
PPOPolicy gradientGeneral purpose, stable
SACActor-criticSample efficient, continuous control
TD3Actor-criticContinuous control, less hyperparameter sensitive
DrQ-v2Image-basedLearning from pixels

Sim-to-Real Transfer

Training RL in the real world is slow and dangerous. Solution: train in simulation, deploy on real robot.

The Sim-to-Real Gap

Simulations don’t perfectly match reality:

  • Physics approximations
  • Sensor noise differences
  • Actuator dynamics

Domain Randomization

Randomize simulation parameters so policy becomes robust:

# Randomize during training
friction = uniform(0.5, 1.5)
mass = uniform(0.8, 1.2) * nominal_mass
observation_noise = normal(0, 0.01)

The policy learns to handle variation, transfers better to real world.

NVIDIA Isaac Sim 5.1 + Isaac Lab 2.3

Terminal window
# Train locomotion policy with RSL-RL
python source/standalone/workflows/rsl_rl/train.py \
    --task Isaac-Velocity-Flat-Anymal-D-v0 \
    --num_envs 4096

Or use the Python API:

from omni.isaac.lab.envs import ManagerBasedRLEnv


env = ManagerBasedRLEnv(
    cfg=robot_cfg,
    num_envs=4096,  # Massive parallelism on GPU
)

Benefits:

  • 4096+ environments in parallel on single GPU (~90,000 FPS training)
  • Photorealistic rendering for vision-based RL
  • Domain randomization built-in with ADR (Automatic Domain Randomization)
  • PBT (Population-Based Training) support

Reward Engineering

The reward function shapes what the robot learns. This is critical and often the hardest part.

Sparse vs Dense Rewards

Sparse: Reward only at goal (task complete = +1, else 0)

  • Hard to learn — random exploration rarely finds goal
  • Clear signal — no reward hacking

Dense: Continuous feedback (distance to goal, velocity toward target)

  • Easier to learn — gradient toward goal
  • Risk of reward hacking — robot finds unintended shortcuts

Reward Shaping Example

def compute_reward(state, action, next_state):
    # Goal: reach target position
    distance = np.linalg.norm(next_state.position - target)


    # Dense reward: closer is better
    reward = -distance


    # Bonus for reaching goal
    if distance < 0.05:
        reward += 100


    # Penalty for excessive force
    reward -= 0.01 * np.sum(action**2)


    return reward

Practical Tips

What Works

  • PPO — Most reliable starting point
  • Simulation — Always start in sim, even for simple tasks
  • Curriculum — Start easy, increase difficulty
  • Reward normalization — Keep rewards in reasonable range

Common Pitfalls

  • Reward hacking — Robot finds unintended way to maximize reward
  • Hyperparameter sensitivity — RL algorithms are notoriously finicky
  • Sample inefficiency — May need millions of steps
  • Sim-to-real gap — Works in sim, fails on real robot

State of the Art (2026)

  • Isaac Lab 2.3 — NVIDIA’s framework for GPU-accelerated robot learning with DexSuite for dexterous manipulation
  • Isaac Lab-Arena — Policy evaluation framework for generalist robot policies
  • Foundation policies — Pre-trained on diverse tasks, fine-tune for specific robot
  • Real-world RL — Becoming more practical with better sim-to-real and safer exploration

Sources