name	offline-rl
description	Master CQL, IQL, BCQ - offline RL from fixed datasets without environment interaction

Offline Reinforcement Learning

When to Use This Skill

Invoke this skill when you encounter:

Offline Dataset: User has fixed dataset D, no environment access
Distribution Shift: Policy improvements diverge from data collection distribution
Value Overestimation: Learning from offline data causes Q-values to diverge
CQL/IQL Implementation: Implementing conservative value estimation
Behavior Cloning Constraints: How to keep policy close to data distribution
BCQ Algorithm: Batch-Constrained Q-learning for offline control
Offline Evaluation: Evaluating policy performance without environment interaction
When to Apply Offline RL: Deciding if offline RL is needed vs online alternatives
Extrapolation Error: Understanding why agents fail on OOD actions
Data Quality Impact: How dataset composition affects algorithm choice

This skill teaches learning from fixed datasets without environment interaction.

Do NOT use this skill for:

Online RL with environment interaction (use policy-gradient-methods, actor-critic-methods)
Pure supervised learning on (s,a) pairs (that's behavior cloning, use supervised learning)
Online model-free learning (use value-based-methods)
Algorithm-agnostic debugging (use rl-debugging-methodology)

Core Principle

Offline RL learns from fixed datasets without environment interaction, solving the fundamental problem of value overestimation without online correction.

The core insight: Standard RL algorithms (Q-learning, policy gradient) assume you can interact with environment to correct mistakes. Offline RL has no such luxury.

Online RL Problem:
  1. Agent explores environment
  2. Collects (s,a,r,s',d) transitions
  3. Updates value estimate: Q[s,a] ← r + γ max_a' Q[s',a']
  4. If Q overestimates, agent tries bad action
  5. Environment gives low reward
  6. Q-value corrects downward in next update

Offline RL Problem:
  1. Agent receives fixed dataset D
  2. Estimates Q from D only
  3. Updates value estimate: Q[s,a] ← r + γ max_a' Q[s',a']
  4. If Q overestimates (no data for some actions), agent tries bad action
  5. No feedback! Can't try action in environment
  6. Q-value never corrects. Error compounds.
  7. Policy diverges, performance collapses

Without understanding extrapolation error and conservative value estimation, you'll implement algorithms that hallucinate value for unseen state-action pairs.

Part 1: The Offline RL Problem

Offline RL Fundamentals

Offline RL Setting:

You have fixed dataset D = {(s_i, a_i, r_i, s'_i, d_i)} collected by unknown behavior policy
No access to environment (can't interact)
Goal: Learn policy π that maximizes expected return
Constraint: Policy must work on real environment, not just in data

Key Difference from Supervised Learning:

Supervised Learning (behavior cloning):
  π = argmin_π E_{(s,a)~D}[||π(a|s) - β(a|s)||²]
  Problem: Learns data collection policy, can't improve

Offline RL:
  π = argmin_π E_{(s,a,r,s')~D}[Q(s,a) - μ(a|s)]
  Benefit: Uses reward signal to improve beyond data
  Challenge: Q-values unreliable outside data distribution

Why Standard Q-Learning Fails on Offline Data

The Extrapolation Problem:

Imagine discrete MDP with 3 actions: left, right, wait.

# Data collection: random policy samples uniformly
# States: {s1, s2}
# Dataset D:
# (s1, left, r=5, s1')
# (s1, left, r=4, s1')
# (s1, right, r=3, s1')
# (s2, wait, r=10, s2')
# (s2, wait, r=9, s2')
# (s1, right, r=2, s1')

# Training Q-Learning on D:
Q(s1, left) ≈ 4.5   # Average of data
Q(s1, right) ≈ 2.5  # Average of data
Q(s1, wait) ≈ ???   # No data! Network must extrapolate

# What does Q-network guess for Q(s1, wait)?
# Network sees:
#   - action=left → reward high
#   - action=right → reward low
#   - action=wait → no signal
# Worst case: network predicts Q(s1, wait) = 100 (hallucination!)

# Policy improvement:
π(a|s1) = argmax_a Q(s1, a)
         = argmax{4.5, 2.5, 100}
         = wait  # WRONG CHOICE!

# In real environment: s1 + wait = crash (reward = -100)
# Why? Network extrapolated wildly beyond training distribution.

The Root Cause:

Training signal only for actions in D
Network trained to minimize MSE on seen (s,a) pairs
For unseen (s,a), network interpolates/extrapolates
Extrapolation is unreliable in high dimensions
Policy picks extrapolated high values
In reality: catastrophic failure

Distribution Shift and Policy Divergence

The Core Challenge:

Initial behavior policy β:
  Collects D by visiting state distribution d_β
  D covers: {actions β takes, rewards β gets}

Your learned policy π:
  Improves on β
  Visits different states: d_π
  Tries different actions: π(a|s)

Mismatch:
  - States in d_π but not d_β: Q has no data
  - Actions π takes but β doesn't: Q extrapolates
  - Q-estimates unreliable
  - Policy gets stuck in hallucinated high-value regions

Example: Robot Manipulation:

Data: Collected by [move_forward, move_left, grasp] policy
- Good at pushing objects forward
- Poor at pulling backward

Your offline-trained π:
- Learns forward motion works (in data)
- Learns backward motion has high Q (extrapolated!)
- Because backward unexplored, network guesses Q=50

Reality:
- Forward: actually good
- Backward: crashes into wall, r=-100
- Policy fails catastrophically

Why?
- Distribution shift: π tries backward, d_π ≠ d_β
- No data for backward actions
- Network extrapolates incorrectly

Value Overestimation: The Central Problem

Standard Q-learning uses:

Q(s,a) ← Q(s,a) + α(r + γ max_{a'} Q(s',a') - Q(s,a))
                            └─ Greedy max

The Problem with max_a':

With limited data:
- max_{a'} Q(s',a') picks highest Q-value
- But if Q overestimates for most actions
- max picks an overestimated value
- TD target is too high
- Q-values drift upward indefinitely

Why Does This Happen Offline?

Online RL:

Iteration 1:
  Q(s,a) = 10 (overestimate)
Iteration 2:
  Real transition: (s,a,r=-5,s')
  Q(s,a) ← -5 + γ max Q(s',a')
  If Q(s',a') corrected to 0, Q(s,a) ← -5
  Overestimation corrected!

Offline RL:
  Same transition in replay buffer D
  Q(s,a) ← -5 + γ max Q(s',a')
  But max Q(s',a') still overestimated (no correction)
  Q stays high, continues overestimating
  Error never corrects

Part 2: Conservative Q-Learning (CQL)

CQL: The Idea

Conservative Q-Learning directly addresses value overestimation by adding a pessimistic lower bound:

Standard Bellman (optimistic):
  Q(s,a) ← r + γ max_{a'} Q(s',a')

CQL (conservative):
  Q(s,a) ← r + γ max_{a'} (Q(s',a') - α * C(a'))

Where C(a') is a penalty for actions outside data distribution.

Key Idea: Penalize high Q-values for actions not well-represented in data.

CQL in Practice: The Implementation

Full CQL Update:

import torch
import torch.nn as nn
from torch.optim import Adam

class CQLAgent:
    def __init__(self, state_dim, action_dim, hidden_dim=256):
        # Q-network (standard DQN-style)
        self.Q = nn.Sequential(
            nn.Linear(state_dim + action_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, 1)  # Q-value output
        )

        # Behavior cloning network (estimate β(a|s))
        self.pi_b = nn.Sequential(
            nn.Linear(state_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, action_dim)
        )

        self.Q_optimizer = Adam(self.Q.parameters(), lr=3e-4)
        self.pi_b_optimizer = Adam(self.pi_b.parameters(), lr=3e-4)

        # CQL hyperparameters
        self.cql_weight = 1.0  # How much to penalize OOD actions
        self.discount = 0.99
        self.target_update_rate = 0.005

        # Target network
        self.Q_target = nn.Sequential(
            nn.Linear(state_dim + action_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, 1)
        )
        self._soft_update_target()

    def train_step(self, batch):
        """
        One CQL training update on batch D.
        Batch contains: states, actions, rewards, next_states, dones
        """
        states, actions, rewards, next_states, dones = batch
        batch_size = states.shape[0]

        # 1. Compute TD target with CQL penalty
        with torch.no_grad():
            # Next action values
            q_next = self.Q_target(torch.cat([next_states, actions], dim=1))

            # CQL: penalize high Q-values for OOD actions
            # Sample random actions and batch actions
            random_actions = torch.rand((batch_size, 10, self.action_dim))
            batch_actions = actions.unsqueeze(1).expand(-1, 10, -1)

            # Q-values for random and batch actions
            q_random = self.Q_target(torch.cat([
                next_states.unsqueeze(1).expand(-1, 10, -1),
                random_actions
            ], dim=2))  # [batch, 10]

            q_batch = self.Q_target(torch.cat([
                next_states.unsqueeze(1).expand(-1, 10, -1),
                batch_actions
            ], dim=2))  # [batch, 10]

            # CQL penalty: log(sum exp(Q_random) + sum exp(Q_batch))
            # This penalizes taking OOD actions
            cql_penalty = (
                torch.logsumexp(q_random, dim=1) +
                torch.logsumexp(q_batch, dim=1)
            )

            # TD target: conservative value estimate
            td_target = rewards + (1 - dones) * self.discount * (
                q_next - self.cql_weight * cql_penalty / 2
            )

        # 2. Update Q-network
        q_pred = self.Q(torch.cat([states, actions], dim=1))
        q_loss = ((q_pred - td_target) ** 2).mean()

        self.Q_optimizer.zero_grad()
        q_loss.backward()
        self.Q_optimizer.step()

        # 3. Update behavior cloning network (optional)
        # Helps estimate which actions are in-distribution
        log_probs = torch.log_softmax(self.pi_b(states), dim=1)
        bc_loss = -log_probs.gather(1, actions.long()).mean()

        self.pi_b_optimizer.zero_grad()
        bc_loss.backward()
        self.pi_b_optimizer.step()

        # 4. Soft update target network
        self._soft_update_target()

        return {
            'q_loss': q_loss.item(),
            'bc_loss': bc_loss.item(),
            'cql_penalty': cql_penalty.mean().item()
        }

    def _soft_update_target(self):
        """Soft update target network toward main network."""
        for target_param, main_param in zip(
            self.Q_target.parameters(),
            self.Q.parameters()
        ):
            target_param.data.copy_(
                self.target_update_rate * main_param.data +
                (1 - self.target_update_rate) * target_param.data
            )

    def select_action(self, state, temperature=0.1):
        """
        Select action using CQL-trained Q-values.
        Temperature controls exploration.
        """
        with torch.no_grad():
            # Evaluate all actions (in discrete case)
            q_values = []
            for a in range(self.action_dim):
                action_tensor = torch.tensor([a], dtype=torch.float32)
                q_val = self.Q(torch.cat([state, action_tensor]))
                q_values.append(q_val.item())

            q_values = torch.tensor(q_values)

            # Softmax policy (temperature for uncertainty)
            logits = q_values / temperature
            action_probs = torch.softmax(logits, dim=0)

            # Sample or take greedy
            action = torch.multinomial(action_probs, 1).item()
            return action

Key CQL Components:

CQL Penalty: logsumexp(Q_random) + logsumexp(Q_batch)
- Penalizes high Q-values for both random and batch actions
- Forces Q-network to be pessimistic
- Prevents extrapolation to unseen actions
Conservative Target: r + γ(Q(s',a') - α*penalty)
- Lowers TD target by CQL penalty amount
- Makes Q-estimates more conservative
- Safer for policy improvement
Behavior Cloning Network: Estimates β(a|s)
- Helps identify in-distribution actions
- Can weight CQL penalty by action probability
- Tighter constraint on constrained actions

CQL Intuition

What CQL Prevents:

Without CQL:
Q(s1, wait) = 100  (hallucinated, no data)
π picks wait → disaster

With CQL:
Q_target for s1, wait includes penalty
Q_target = r + γ(Q(s',a') - α * log(sum exp(Q)))
         = r + γ(50 - 100)
         = r - 50 * γ
CQL pessimism forces Q(s1, wait) low
π picks safer action

Result: Policy stays in data distribution, avoids hallucinated values

When CQL Works Well

Short horizons: Errors don't compound as much
Diverse data: Multiple actions represented
Known behavior policy: Can weight penalty appropriately
Discrete actions: Easier to evaluate all actions

CQL Failure Modes

Too conservative on good data: May not improve over β
High variance penalties: log-sum-exp can be unstable
Computational cost: Requires sampling many actions

Part 3: Implicit Q-Learning (IQL)

IQL: A Different Approach to Pessimism

While CQL explicitly penalizes OOD actions, IQL achieves pessimism through a different mechanism: expectile regression.

Standard L2 Regression (mean):
  Expected value minimizes E[(y - ŷ)²]

Expectile Regression (quantile-like):
  Expects value minimizes E[|2τ - 1| * |y - ŷ|] for τ in (0,1)
  - τ < 0.5: underestimates (pessimistic)
  - τ = 0.5: median (neutral)
  - τ > 0.5: overestimates (optimistic)

IQL Implementation

Key Insight: Use expectile loss to make Q-estimates naturally pessimistic without explicit penalties.

class IQLAgent:
    def __init__(self, state_dim, action_dim, hidden_dim=256, expectile=0.7):
        self.Q = self._build_q_network(state_dim, action_dim, hidden_dim)
        self.V = self._build_v_network(state_dim, hidden_dim)  # Value function

        self.Q_optimizer = Adam(self.Q.parameters(), lr=3e-4)
        self.V_optimizer = Adam(self.V.parameters(), lr=3e-4)

        self.expectile = expectile  # τ = 0.7 for slight pessimism
        self.discount = 0.99
        self.temperature = 1.0  # For policy softness

    def expectile_loss(self, diff, expectile):
        """
        Asymmetric expectile loss.
        Penalizes overestimation more than underestimation (pessimism).
        """
        weight = torch.where(
            diff > 0,
            expectile * torch.ones_like(diff),
            (1 - expectile) * torch.ones_like(diff)
        )
        return weight * (diff ** 2)

    def train_v_function(self, batch):
        """
        Step 1: Train value function V(s)
        V(s) estimates expected Q-value under behavior policy
        """
        states, actions, rewards, next_states, dones = batch

        # Q-values from current policy
        q_values = self.Q(states, actions)

        # V-network predicts these Q-values
        v_pred = self.V(states)

        # Expectile loss: V should underestimate Q slightly
        # (stay pessimistic)
        q_diff = q_values - v_pred
        v_loss = self.expectile_loss(q_diff, self.expectile).mean()

        self.V_optimizer.zero_grad()
        v_loss.backward()
        self.V_optimizer.step()

        return {'v_loss': v_loss.item()}

    def train_q_function(self, batch):
        """
        Step 2: Train Q-function using pessimistic V-target
        Q(s,a) ← r + γ V(s')  (instead of γ max_a' Q(s',a'))
        """
        states, actions, rewards, next_states, dones = batch

        # IQL target: use V-function instead of max Q
        with torch.no_grad():
            v_next = self.V(next_states)
            td_target = rewards + (1 - dones) * self.discount * v_next

        q_pred = self.Q(states, actions)
        q_loss = ((q_pred - td_target) ** 2).mean()

        self.Q_optimizer.zero_grad()
        q_loss.backward()
        self.Q_optimizer.step()

        return {'q_loss': q_loss.item()}

    def train_step(self, batch):
        """IQL training: V-function first, then Q-function."""
        v_info = self.train_v_function(batch)
        q_info = self.train_q_function(batch)
        return {**v_info, **q_info}

    def select_action(self, state):
        """
        Policy improvement: use exponential weighted Q-values.
        Only improve actions with high estimated value.
        """
        with torch.no_grad():
            # Evaluate actions
            q_values = self.Q(state, actions=None)  # All actions

            # Exponential weighting: exp(Q/τ)
            # Concentrates on high-Q actions
            weights = torch.exp(q_values / self.temperature)
            weights = weights / weights.sum()

            action = torch.multinomial(weights, 1).item()
            return action

IQL Key Insight

Why V-function Makes Q Pessimistic:

Standard Q-Learning:
  Q(s,a) = r + γ max_a' Q(s',a')  ← Optimistic!

IQL:
  1. Train V(s) to estimate Q under behavior policy
     V(s) ≈ E_a~β[Q(s,a)]
  2. Use V as target: Q(s,a) = r + γ V(s')
     Why pessimistic?

     If policy is suboptimal:
       - Good actions: Q > V(s) (above average)
       - Bad actions: Q < V(s) (below average)
       - max_a Q(s',a') picks good action (might extrapolate)
       - V(s') is average (conservative)

    Result: Using V instead of max prevents picking overestimated actions!

Expectile Loss Intuition

Standard MSE: E[(Q - V)²]
  - Symmetric penalty: overestimation = underestimation

Expectile Loss (τ=0.7): E[|2*0.7 - 1| * |Q - V|²]
  - When Q > V: weight = 0.7 (moderate penalty)
  - When Q < V: weight = 0.3 (light penalty)
  - Result: V underestimates Q slightly

Effect: Q values are naturally pessimistic without explicit penalties!

When IQL Excels

High-dimensional observations: V-function is simpler than Q
Continuous actions: No need to discretize
Mixed quality data: Expectile naturally handles varying data quality
Implicit distribution shift handling: V-function implicitly constrains to data distribution

Part 4: Batch-Constrained Q-Learning (BCQ)

BCQ: Constraining Policy to Behavior Support

Core Idea: Only improve actions that have high probability under behavior policy β.

Standard offline improvement:
  π(a|s) ← exp(Q(s,a) / τ)  ← Can pick any action!

BCQ improvement:
  π(a|s) ← exp(Q(s,a) / τ) * I(β(a|s) > threshold)
           └─ Only if action has nonzero β probability

BCQ Implementation

class BCQAgent:
    def __init__(self, state_dim, action_dim, hidden_dim=256):
        # Q-network
        self.Q = self._build_q_network(state_dim, action_dim, hidden_dim)

        # Behavior cloning network: learns β(a|s)
        self.pi_b = self._build_policy_network(state_dim, action_dim, hidden_dim)

        # Perturbation network: learn action perturbations near β
        # π(a|s) = arg max_a Q(s, β(s) + ξ(s, a))
        self.perturbation = self._build_perturbation_network(
            state_dim, action_dim, hidden_dim
        )

        self.Q_optimizer = Adam(self.Q.parameters(), lr=3e-4)
        self.pi_b_optimizer = Adam(self.pi_b.parameters(), lr=3e-4)
        self.perturbation_optimizer = Adam(self.perturbation.parameters(), lr=3e-4)

        self.discount = 0.99
        self.constraint_weight = 0.5  # How strict is batch constraint?

    def train_step(self, batch):
        """BCQ training loop."""
        states, actions, rewards, next_states, dones = batch

        # 1. Train behavior cloning network β
        log_probs = torch.log_softmax(self.pi_b(states), dim=1)
        pi_b_loss = -log_probs.gather(1, actions.long()).mean()

        self.pi_b_optimizer.zero_grad()
        pi_b_loss.backward()
        self.pi_b_optimizer.step()

        # 2. Train Q-network with BCQ constraint
        with torch.no_grad():
            # Behavior actions in next state
            pi_b_next = self.pi_b(next_states)  # [batch, action_dim]

            # Perturbation network learns small deviations from β
            perturbation = self.perturbation(next_states, pi_b_next)

            # Constrained action: π(a|s') = β(s') + ξ(s')
            # But in action space [-1, 1] (clipped)
            constrained_actions = torch.clamp(
                pi_b_next + perturbation, min=-1, max=1
            )

            # Q-value with constrained action
            q_next = self.Q(next_states, constrained_actions)
            td_target = rewards + (1 - dones) * self.discount * q_next

        # Q-loss
        q_pred = self.Q(states, actions)
        q_loss = ((q_pred - td_target) ** 2).mean()

        self.Q_optimizer.zero_grad()
        q_loss.backward()
        self.Q_optimizer.step()

        # 3. Train perturbation network
        # Learn to find best perturbation to β that increases Q
        pi_b_curr = self.pi_b(states)
        perturbation_curr = self.perturbation(states, pi_b_curr)
        perturbed_actions = torch.clamp(
            pi_b_curr + perturbation_curr, min=-1, max=1
        )

        q_perturbed = self.Q(states, perturbed_actions)
        perturbation_loss = -q_perturbed.mean()  # Maximize Q

        self.perturbation_optimizer.zero_grad()
        perturbation_loss.backward()
        self.perturbation_optimizer.step()

        return {
            'q_loss': q_loss.item(),
            'pi_b_loss': pi_b_loss.item(),
            'perturbation_loss': perturbation_loss.item()
        }

    def select_action(self, state, num_samples=100):
        """
        BCQ action selection:
        1. Sample from β(a|s)
        2. Perturb by small amount
        3. Pick action with highest Q
        """
        with torch.no_grad():
            # Behavior policy action
            pi_b = self.pi_b(state)

            # Sample small perturbations
            perturbations = []
            for _ in range(num_samples):
                pert = self.perturbation(state, pi_b)
                perturbed = torch.clamp(pi_b + pert, min=-1, max=1)
                perturbations.append(perturbed)

            perturbations = torch.stack(perturbations)  # [num_samples, action_dim]

            # Evaluate Q for each perturbation
            q_values = []
            for pert_action in perturbations:
                q_val = self.Q(state, pert_action)
                q_values.append(q_val.item())

            # Pick action with highest Q
            best_idx = np.argmax(q_values)
            action = perturbations[best_idx]

            return action

BCQ Core Mechanism

Batch Constraint Principle:

Without BCQ:
  Policy π can pick ANY action
  Problem: Picks actions not in dataset
  Result: Q overestimates for unseen actions

With BCQ:
  Policy π = β(s) + small_perturbation(s)
  Constraint: π must stay close to β
  Result: Only slightly improves on data actions

Why it works:
  - Q is accurate for β-like actions (in data)
  - Perturbations are small (confidence is high)
  - Policy can't escape to hallucinated region
  - Safe policy improvement

When BCQ is Appropriate

Very limited data: Strict constraint needed
Expert data with mistakes: Learn expert actions ± small perturbations
Safety-critical: Can't risk exploring OOD actions
Discrete action space: Easier to enumerate nearby actions

BCQ Pitfalls

Too restrictive: Can't leverage good data effectively
Assumes β is reasonable: If β is terrible, BCQ learns terrible policy
Perturbation network complexity: Another network to train

Part 5: Distribution Shift and Offline Evaluation

Understanding Distribution Shift in Offline RL

The Core Challenge:

Training distribution:
  States visited by β (behavior policy)
  Actions β typically takes
  Transitions β experiences

Evaluation distribution:
  States visited by π (learned policy)
  Actions π learns to take
  Transitions π will experience

Problem:
  Training and evaluation distributions diverge
  π will visit states not in training data
  Q-estimates unreliable for those states

Offline Evaluation Without Environment Interaction

The Problem:

Online RL:
  π ← train(D)
  eval_return = run_policy(π, env, episodes=100)
  Simple and accurate

Offline RL:
  π ← train(D)
  Can't run in environment (offline constraint!)
  Must estimate return without interaction
  How to evaluate without cheating?

Offline Evaluation Methods

Method 1: Importance Sampling

def importance_sampling_eval(policy_pi, dataset_D):
    """
    Estimate E[G_π] using importance sampling.

    Key idea:
    E[G_π] = E_{(s,a,...)~π}[G]
           = E_{(s,a,...)~β}[G * (π(a|s)/β(a|s))]

    Use dataset transitions weighted by policy ratio.
    """
    total_return = 0
    total_weight = 0

    for trajectory in dataset_D:
        # Trajectory importance ratio
        traj_ratio = 1.0
        traj_return = 0

        for t, (s, a, r) in enumerate(trajectory):
            # Importance weight for this action
            pi_prob = policy_pi.log_prob(a, s).exp()
            beta_prob = behavior_policy.log_prob(a, s).exp()

            weight = pi_prob / (beta_prob + 1e-8)

            # Importance ratio for trajectory
            traj_ratio *= weight

            # Accumulate return
            traj_return += (0.99 ** t) * r

        # Weight trajectory by importance ratio
        total_return += traj_ratio * traj_return
        total_weight += traj_ratio

    # Average weighted return
    estimated_return = total_return / total_weight
    return estimated_return

Problem: High variance with small β probability. Weight explodes.

Method 2: Regression Importance Sampling (RIS)

def ris_eval(policy_pi, dataset_D, value_fn):
    """
    Regression Importance Sampling: combines IS with value function
    to reduce variance.

    Idea: Use learned V-function for long horizons, IS for short.
    """
    total_return = 0
    total_weight = 0

    for trajectory in dataset_D:
        for t, (s, a, r, s_next) in enumerate(trajectory):
            # Importance weight for this step
            pi_prob = policy_pi.log_prob(a, s).exp()
            beta_prob = behavior_policy.log_prob(a, s).exp()

            is_weight = pi_prob / (beta_prob + 1e-8)

            # Value of next state
            v_next = value_fn(s_next)

            # Hybrid estimate: IS-weighted reward + V-estimate of rest
            return_est = r + 0.99 * v_next
            weighted_return = is_weight * return_est

            total_return += weighted_return
            total_weight += is_weight

    return total_return / total_weight

Method 3: Model-based Estimation

def model_based_eval(policy_pi, dataset_D, dynamics_model):
    """
    Use learned dynamics model to estimate policy performance.

    Idea: π(s) → a, dynamics_model(s,a) → s', Q(s,a) → r
    """
    initial_states = dataset_D.sample_initial_states(batch_size=100)

    total_return = 0

    for s in initial_states:
        traj_return = 0
        done = False

        for t in range(horizon):
            # Policy action
            a = policy_pi.select_action(s)

            # Model prediction
            s_next = dynamics_model(s, a)
            r = reward_fn(s, a)  # or Q(s,a)

            # Accumulate return
            traj_return += (0.99 ** t) * r

            s = s_next
            if done:
                break

        total_return += traj_return

    estimated_return = total_return / len(initial_states)
    return estimated_return

Offline Evaluation Challenges

Challenge 1: Importance Weight Explosion
  - If β rarely takes action π prefers
  - IS weight = π(a|s)/β(a|s) becomes huge
  - Estimate has infinite variance

Challenge 2: V-Function Errors
  - If V-function incorrect
  - RIS estimates still wrong
  - Can't correct without environment feedback

Challenge 3: Model Errors
  - If dynamics model wrong
  - Model-based estimates diverge from reality
  - Especially bad for long horizons

Solution: Use multiple methods, cross-validate
  - If all methods agree: estimate is reliable
  - If methods disagree: be suspicious, try online validation

Part 6: When Offline RL is Needed

Decision Framework: Offline vs Online RL

Question 1: Can you collect more data?

YES → Consider online RL
  - Use environment interaction
  - Standard algorithms work well
  - Lower algorithmic complexity

NO → Offline RL necessary
  - Fixed dataset only
  - Learn without interaction
  - Handle overestimation explicitly

Question 2: Is data collection expensive?

YES (expensive) → Offline RL pays off
  - Robot experiments: $1000+ per hour
  - Medical trials: ethical constraints
  - Real-world deployment: safety concerns

NO (cheap) → Online RL usually better
  - Simulation available
  - Data generation easy
  - Self-play systems

Question 3: Is data collection dangerous?

YES → Offline RL + careful validation
  - Autonomous driving
  - Nuclear plants
  - Medical systems
  - Learn conservatively from past experience

NO → Online RL fine
  - Game environments
  - Safe simulators
  - Can afford exploration failures

When Offline RL is Essential

1. Real-World Robotics

Problem: Collect trajectory = robot experiment
Cost: $$$, time, expertise
Solution: Offline RL from logged demonstrations

Example: Learning from human tele-operation logs
- Data collection: humans control robot
- Training: offline RL from logs
- Deployment: robot improves on human behavior

Why offline RL helps:
- Can't try random actions (breaks hardware)
- Can't explore unsafely (danger)
- Limited budget for experiments

2. Medical Treatment Policies

Problem: Can't experiment on patients
Data: Historical patient records
Solution: Offline RL to find better treatments

Example: Learning antibiotic treatment policies
- Data: patient → treatment → outcome logs
- Training: offline RL from historical data
- Deployment: recommend treatments

Why offline RL helps:
- Can't do random exploration (unethical)
- Patient outcomes matter immediately
- Limited patient population

3. Recommendation Systems

Problem: Users leave if recommendations bad
Data: Historical user interactions
Solution: Offline RL to improve recommendations

Example: Movie recommendations
- Data: user watches movie → rates it
- Training: offline RL from interaction logs
- Deployment: recommend movies offline users will like

Why offline RL helps:
- Online experiment = worse user experience
- Can't A/B test extensively (business impact)
- Massive data available (can be offline)

When Online RL is Better

1. Simulation Available

Example: Atari games
- Infinite free samples
- Can explore safely
- Rewards deterministic
- Online RL solves it easily

Why offline RL unnecessary:
- Data collection cost ≈ 0
- Exploration safe (it's a game)
- Online algorithms highly optimized

2. Self-Play Systems

Example: Chess, Go
- Generate own data
- Unlimited exploration budget
- Learn strong policies easily
- Online RL natural

Why offline RL adds complexity:
- Data generation is free (self-play)
- Can afford to explore
- Online algorithms work better

3. Simulator Fidelity is High

Example: Training in simulation, deploy in reality
- Simulator accurate enough
- Can collect unlimited data
- Distribution shift minimal (sim matches reality)
- Online RL sufficient

Why offline RL unnecessary:
- Can collect all needed data in simulation
- Don't have distribution shift problem

Part 7: Common Pitfalls and Red Flags

Pitfall 1: Assuming Online Algorithm Will Work

Red Flag: "I'll just use DQN/PPO on my offline data."

Reality: Will overestimate values, learn poor policy.

Example:

Dataset: suboptimal human demonstrations
DQN trained on D: max Q estimated for unseen actions
Result: Policy picks actions never in dataset
Reality: actions fail in deployment

Correct approach:
- Use CQL/IQL to address overestimation
- Constrain policy to behavior support (BCQ)
- Evaluate carefully offline before deployment

Pitfall 2: Ignoring Distribution Shift

Red Flag: "Policy divergence shouldn't be a problem if data is diverse."

Reality: Even diverse data has gaps. Policy will find them.

Example:

Dataset: collected over 6 months, diverse actions
Your policy: learns to combine actions in novel ways
Result: visits unseen state combinations
Q-estimates fail for combinations not in data

Correct approach:
- Monitor policy divergence from data
- Use uncertainty estimates (ensemble Q-networks)
- Gradually deploy, validate offline metrics

Pitfall 3: Evaluating Offline with Online Metrics

Red Flag: "I trained the policy, let me just run it in the environment to evaluate."

Reality: That's not offline RL anymore! Defeats the purpose.

Example:

Offline RL goal: learn without environment interaction
Wrong evaluation: run π in environment 1000 times
Result: uses 1000s of samples for evaluation

Correct approach:
- Use offline evaluation methods (IS, RIS, model-based)
- Validate offline estimates before deployment
- Use conservative estimates (pessimistic evaluation)

Pitfall 4: Not Considering Data Quality

Red Flag: "My algorithm is robust, it handles any data."

Reality: Offline RL performance depends critically on data quality.

Example:

Good data: expert demonstrations, well-explored actions
CQL: performs well, conservative works fine

Bad data: random exploration, sparse rewards
CQL: learns very slowly (too pessimistic)
BCQ: learns random behavior (constrained to β)

Solution: Analyze data quality first
- Percent expert vs random actions
- Return distribution
- Action coverage
- Choose algorithm for data type

Pitfall 5: Overestimating Conservatism

Red Flag: "I'll use maximum pessimism to be safe."

Reality: Too much pessimism prevents learning anything.

Example:

Hyperparameter: CQL weight α = 1000 (extreme)
Result: Q(s,a) always negative (super pessimistic)
Policy: learns random actions (all have low Q)
Performance: no improvement over random

Correct approach:
- Tune conservatism to data quality
- Diverse data: less pessimism (CQL weight = 0.1)
- Limited data: more pessimism (CQL weight = 1.0)
- Validate offline evaluation metrics

Pitfall 6: Forgetting Batch Constraints

Red Flag: "CQL handles distribution shift, I don't need behavior cloning."

Reality: CQL addresses overestimation, not policy divergence.

Example:

CQL alone:
Q-values conservative (good)
Policy gradient: π(a|s) = exp(Q(s,a) / τ)
Policy can still diverge far from β (bad)
New states visited, Q unreliable

CQL + behavior cloning:
Q-values conservative
Policy constrained: π ≈ β + improvement
Policy stays near data
Safe exploration

Solution: Combine approaches
- CQL for value estimation
- KL constraint for policy divergence
- Behavior cloning for explicit constraint

Pitfall 7: Using Wrong Evaluation Metric

Red Flag: "I'll just report average Q-value as evaluation metric."

Reality: Q-values can be hallucinated!

Example:

High Q-values don't mean high returns
- If Q overestimates: high Q, low actual return
- If data is poor: high Q, bad actions

Correct metrics:
- Importance sampling estimate (IS)
- Regression IS estimate (RIS, lower variance)
- Model-based estimate (if model good)
- Conservative: min of multiple estimates

Pitfall 8: Not Handling Batch Imbalance

Red Flag: "I have 1M samples, that's enough data."

Reality: Sample diversity matters more than quantity.

Example:

Dataset composition:
- 900K samples: random actions
- 100K samples: expert demonstrations

Training:
- Q-network sees mostly random behavior
- Expert actions are rare
- Q overestimates expert actions (underfitting them)

Solution:
- Stratified sampling (balance expert/random)
- Importance weighting (weight rare samples higher)
- Separate Q-networks for different behavior types

Pitfall 9: Assuming Stationary Environment

Red Flag: "Environment hasn't changed, my offline policy should work."

Reality: Without online validation, you can't detect environment shifts.

Example:

Training period: robot arm dynamics stable
Deployment period: arm friction increased
Offline estimate: predicted 50 reward
Actual return: 20 reward

Why? Q-function trained on different dynamics.

Solution:
- Monitor deployment performance vs offline estimate
- If large gap: retrain on new data
- Use online validation after deployment

Pitfall 10: Not Accounting for Reward Uncertainty

Red Flag: "Rewards are fixed, no uncertainty."

Reality: Sparse/noisy rewards create uncertainty in Q-estimates.

Example:

Sparse reward environment:
- Most transitions have r = 0
- Few transitions have r = 1 (sparse signal)
- Q-function must extrapolate from few examples

CQL without reward uncertainty:
- Pessimistic on actions, not rewards
- Misses that some Q-high states might have low actual reward

Solution:
- Ensemble Q-networks (estimate uncertainty)
- Use epistemic uncertainty in policy improvement
- Conservative value averaging (use min over ensemble)

Part 8: Real-World Case Studies

Case Study 1: Robot Manipulation

Problem: Train robot to pick and place objects from demonstrations.

Data:
- 50 human teleoperation episodes (~500 transitions each)
- Expert actions: picking, placing, moving
- Cost: each episode = 1 hour robot time

Why offline RL:
- Data collection expensive (human time)
- Can't explore randomly (break hardware)
- Want to improve on expert (BC alone isn't enough)

Solution: IQL
- Learns from expert demonstrations
- V-function constraints policy to expert-like state distribution
- Iteratively improves on expert with confidence

Results:
- Offline training: 2 hours GPU
- Online validation: 1 hour robot time
- Policy: 50% success rate improvement over expert

Case Study 2: Recommendation Systems

Problem: Improve movie recommendations from historical user interactions.

Data:
- 100M user-movie interactions (1 year history)
- Reward: user watches movie (r=1) or skips (r=0)
- Distribution: mix of random recommendations and human editorial

Why offline RL:
- Online A/B test = worse user experience
- Can't explore randomly (hurts engagement metrics)
- But can learn better recommendations from behavior

Solution: CQL
- Conservatively estimates Q(user, movie)
- Avoids recommending movies only hypothetically good
- Safely recommends movies similar to successful past

Results:
- Offline metrics: 20% improvement over heuristics
- A/B test: 8% improvement (offline was too conservative)
- Trade-off: offline pessimism vs online optimality

Case Study 3: Medical Treatment Policy

Problem: Learn treatment policy from patient records to minimize mortality.

Data:
- 50K patient records (diverse treatments, outcomes)
- Actions: drug choices, dosages, procedures
- Reward: patient survives (r=1) or dies (r=-100)

Why offline RL:
- Can't experiment on patients (unethical)
- Sparse reward (most patients live)
- Distribution shift (changing patient population)

Solution: BCQ + IQL hybrid
- BCQ: only improve on treatments in data (avoid new untested drugs)
- IQL: expectile regression handles sparse rewards naturally
- Conservative deployment: start with small population, validate

Challenges:
- Data collection bias (sicker patients get more aggressive treatment)
- Measurement error (outcomes uncertain)
- Non-stationarity (medical practices evolve)

Results:
- Offline validation: policy seems better
- Pilot deployment: 2% improvement
- Requires continuous retraining as new data arrives

Part 9: Advanced Topics

Topic 1: Conservative Q-Learning Variants

CQL with Importance Weighting:

Instead of treating all actions equally in CQL penalty,
weight by behavioral policy probability:

CQL loss = -α * E[(1-β(a|s))/(1+β(a|s)) * Q(s,a)]
           + E[Q(s,a) - target]

Intuition: Heavy penalty on unlikely actions, light penalty on likely ones
Result: More efficient use of data, can improve more than standard CQL

Weighted CQL for Reward Maximization:

Modify target to emphasize high-reward trajectories:

CQL loss = -α * E[weight(r) * Q(s,a)] + E[(Q - target)²]

where weight(r) = high if r is high, low if r is low

Result: Faster learning from expert demonstrations
Trade-off: Less conservative, more risk of overestimation

Topic 2: Offline RL with Function Approximation Errors

When using neural networks, approximation errors can compound:

Error compound formula:
  Total error ≈ sum of:
  1. Bellman error: |r + γ V(s') - Q(s,a)|
  2. Approximation error: |Q_approx(s,a) - Q_true(s,a)|
  3. Extrapolation error: error magnitude grows with |s - data_states|

Solutions:
- Use ensemble networks (estimate uncertainty)
- Conservative value updates (take min over ensemble)
- Explicit uncertainty penalties

Topic 3: Offline-to-Online Fine-Tuning

Real-world often requires offline pre-training + online fine-tuning:

Phase 1: Offline training
  - Learn from fixed dataset
  - Use CQL/IQL for value stability

Phase 2: Online fine-tuning
  - Collect new data with learned policy
  - Fine-tune with reduced exploration (avoid undoing offline learning)
  - Use importance weighting to not forget offline data

Example: Robotics
  - Offline: learn from 1 month of demonstrations
  - Online: 1 week fine-tuning with real environment
  - Combined: policy leverages both demonstrations and interaction

Part 10: Debugging and Diagnosis

Diagnostic Question 1: Are Q-Values Reasonable?

How to Check:

# Sample random trajectory from dataset
state = dataset.sample_state()
for action in range(num_actions):
    q_val = Q(state, action)
    print(f"Q({state}, {action}) = {q_val}")

# Reasonable bounds:
# - Q-values should match observed returns (~10-100 for most tasks)
# - Not astronomical (1000+) without justification
# - Negative only if task is hard

Red Flags:

Q-values > 1000: overestimation (increase CQL weight)
Q-values all negative: pessimism too high (decrease CQL weight)
Q-values constant: network not learning (check training loss)

Diagnostic Question 2: Is Policy Diverging from Behavior?

How to Check:

# Compute KL divergence between learned π and behavior β
kl_div = 0
for state in test_states:
    pi_logprobs = π.log_prob(state)
    beta_logprobs = β.log_prob(state)
    kl_div += (pi_logprobs.exp() * (pi_logprobs - beta_logprobs)).mean()

# Reasonable KL divergence:
# - < 1.0 bit: small divergence (safe)
# - 1.0-5.0 bits: moderate divergence (watch for OOD)
# - > 5.0 bits: severe divergence (use more regularization)

Fixes:

Too much divergence: increase KL constraint weight
Too little divergence: decrease constraint (not learning)

Diagnostic Question 3: Offline vs Online Performance Gap

# Estimate offline performance (no environment interaction)
offline_return_estimate = importance_sampling_eval(policy_π, dataset)

# Estimate online performance (if possible to test)
online_return_actual = run_policy(policy_π, environment, episodes=10)

# Gap analysis:
gap = online_return_actual - offline_return_estimate

# Interpretation:
# gap ≈ 0: offline estimates reliable
# gap > 10%: offline estimate optimistic (increase pessimism)
# gap < -10%: offline estimate too pessimistic (decrease pessimism)

Conclusion: When You Know Offline RL

You understand offline RL when you can:

Explain value overestimation without environment feedback
Choose between CQL, IQL, BCQ for data type
Recognize distribution shift in your learned policy
Design offline evaluation without environment rollouts
Know when offline RL worth the complexity
Diagnose training failures (overestimation, divergence, etc.)

Next Steps:

Implement CQL from scratch on GridWorld
Compare CQL vs IQL vs BCQ on offline benchmark
Design offline evaluation strategy for your domain
Deploy cautiously: offline → online fine-tuning

References & Further Learning

Key Papers:

CQL: "Conservative Q-Learning for Offline Reinforcement Learning" (Kumar et al., 2020)
IQL: "Offline Reinforcement Learning with Implicit Q-Learning" (Kostrikov et al., 2021)
BCQ: "Batch-Constrained Deep Q-Learning" (Fujimoto et al., 2018)

Offline RL Benchmarks:

D4RL: Offline RL benchmark suite
Atari 5M: Limited sample offline Atari
Locomotion: MuJoCo continuous control

Related Skills:

rl-foundations: TD learning, Bellman equations
value-based-methods: Q-learning fundamentals
policy-gradient-methods: Policy improvement
rl-evaluation-benchmarking: How to measure RL progress

Install Skill

SKILL.md

Offline Reinforcement Learning

When to Use This Skill

Core Principle

Part 1: The Offline RL Problem

Offline RL Fundamentals

Why Standard Q-Learning Fails on Offline Data

Distribution Shift and Policy Divergence

Value Overestimation: The Central Problem

Part 2: Conservative Q-Learning (CQL)

CQL: The Idea

CQL in Practice: The Implementation

CQL Intuition

When CQL Works Well

CQL Failure Modes

Part 3: Implicit Q-Learning (IQL)

IQL: A Different Approach to Pessimism

IQL Implementation

IQL Key Insight

Expectile Loss Intuition

When IQL Excels

Part 4: Batch-Constrained Q-Learning (BCQ)

BCQ: Constraining Policy to Behavior Support

BCQ Implementation

BCQ Core Mechanism

When BCQ is Appropriate

BCQ Pitfalls

Part 5: Distribution Shift and Offline Evaluation

Understanding Distribution Shift in Offline RL

Offline Evaluation Without Environment Interaction

Offline Evaluation Methods

Offline Evaluation Challenges

Part 6: When Offline RL is Needed

Decision Framework: Offline vs Online RL

When Offline RL is Essential

When Online RL is Better

Part 7: Common Pitfalls and Red Flags

Pitfall 1: Assuming Online Algorithm Will Work

Pitfall 2: Ignoring Distribution Shift

Pitfall 3: Evaluating Offline with Online Metrics

Pitfall 4: Not Considering Data Quality

Pitfall 5: Overestimating Conservatism

Pitfall 6: Forgetting Batch Constraints

Pitfall 7: Using Wrong Evaluation Metric

Pitfall 8: Not Handling Batch Imbalance

Pitfall 9: Assuming Stationary Environment

Pitfall 10: Not Accounting for Reward Uncertainty

Part 8: Real-World Case Studies

Case Study 1: Robot Manipulation

Case Study 2: Recommendation Systems

Case Study 3: Medical Treatment Policy

Part 9: Advanced Topics

Topic 1: Conservative Q-Learning Variants

Topic 2: Offline RL with Function Approximation Errors

Topic 3: Offline-to-Online Fine-Tuning

Part 10: Debugging and Diagnosis

Diagnostic Question 1: Are Q-Values Reasonable?

Diagnostic Question 2: Is Policy Diverging from Behavior?

Diagnostic Question 3: Offline vs Online Performance Gap

Conclusion: When You Know Offline RL

References & Further Learning