data/derived/chapter_hp_mapping.md

# Chapter-to-HP Mapping

For each chapter, identify which config fields have >1 unique value (swept)
vs exactly 1 value (fixed).

## Chap1 (8100 runs)

**Swept HPs:**
  - `num_envs`: 5 levels — [1024, 128, 256, 512, 64]
  - `rollout_length`: 5 levels — [1024, 128, 256, 512, 64]
  - `clip_epsilon`: 3 levels — [0.01, 0.2, 0.4]
  - `loss_weights.critic_loss`: 3 levels — [0.5, 0.85, 1.0]
  - `loss_weights.actor_entropy`: 3 levels — [-0.001, -0.01, -0.1]

**Fixed HPs:**
  - `discount` = 0.99
  - `gae_lambda` = 0.95
  - `gae_horizon` = 0
  - `loss_weights.actor_loss` = 1
  - `optimizer.lr` = 0.0003
  - `optimizer.grad_clip_norm` = 1.0
  - `normalize_obs` = True
  - `normalize_gae` = True
  - `minibatch_size` = 128
  - `reuse_rollout_epochs` = 4
  - `agent_network.actor_hidden_layer_sizes` = [256, 256]
  - `agent_network.critic_hidden_layer_sizes` = [256, 256]

## Chap2 (8100 runs)

**Swept HPs:**
  - `num_envs`: 5 levels — [1024, 128, 256, 512, 64]
  - `rollout_length`: 5 levels — [1024, 128, 256, 512, 64]
  - `discount`: 3 levels — [0.99, 0.995, 0.999]
  - `gae_lambda`: 3 levels — [0.7, 0.8, 0.95]
  - `gae_horizon`: 3 levels — [0, 32, 64]

**Fixed HPs:**
  - `clip_epsilon` = 0.2
  - `loss_weights.actor_loss` = 1
  - `loss_weights.critic_loss` = 0.5
  - `loss_weights.actor_entropy` = -0.01
  - `optimizer.lr` = 0.0003
  - `optimizer.grad_clip_norm` = 1.0
  - `normalize_obs` = True
  - `normalize_gae` = True
  - `minibatch_size` = 128
  - `reuse_rollout_epochs` = 4
  - `agent_network.actor_hidden_layer_sizes` = [256, 256]
  - `agent_network.critic_hidden_layer_sizes` = [256, 256]

## Chap3 (4800 runs)

**Swept HPs:**
  - `num_envs`: 5 levels — [1024, 128, 256, 512, 64]
  - `rollout_length`: 5 levels — [1024, 128, 256, 512, 64]
  - `optimizer.grad_clip_norm`: 4 levels — [0.0, 0.5, 1.0, 5.0]
  - `normalize_obs`: 2 levels — [False, True]
  - `normalize_gae`: 2 levels — [False, True]

**Fixed HPs:**
  - `discount` = 0.99
  - `gae_lambda` = 0.95
  - `gae_horizon` = 0
  - `clip_epsilon` = 0.2
  - `loss_weights.actor_loss` = 1
  - `loss_weights.critic_loss` = 0.5
  - `loss_weights.actor_entropy` = -0.01
  - `optimizer.lr` = 0.0003
  - `minibatch_size` = 128
  - `reuse_rollout_epochs` = 4
  - `agent_network.actor_hidden_layer_sizes` = [256, 256]
  - `agent_network.critic_hidden_layer_sizes` = [256, 256]

## Chap4 (8100 runs)

**Swept HPs:**
  - `num_envs`: 5 levels — [1024, 128, 256, 512, 64]
  - `rollout_length`: 5 levels — [1024, 128, 256, 512, 64]
  - `optimizer.lr`: 3 levels — [0.0003, 0.003, 3e-05]
  - `minibatch_size`: 3 levels — [1024, 128, 512]
  - `reuse_rollout_epochs`: 3 levels — [1, 4, 8]

**Fixed HPs:**
  - `discount` = 0.99
  - `gae_lambda` = 0.95
  - `gae_horizon` = 0
  - `clip_epsilon` = 0.2
  - `loss_weights.actor_loss` = 1
  - `loss_weights.critic_loss` = 0.5
  - `loss_weights.actor_entropy` = -0.01
  - `optimizer.grad_clip_norm` = 1.0
  - `normalize_obs` = True
  - `normalize_gae` = True
  - `agent_network.actor_hidden_layer_sizes` = [256, 256]
  - `agent_network.critic_hidden_layer_sizes` = [256, 256]

## Chap5 (8100 runs)

**Swept HPs:**
  - `num_envs`: 5 levels — [1024, 128, 256, 512, 64]
  - `rollout_length`: 5 levels — [1024, 128, 256, 512, 64]
  - `optimizer.lr`: 3 levels — [0.0003, 0.003, 3e-05]
  - `agent_network.actor_hidden_layer_sizes`: 3 levels — ['[256, 256, 256]', '[256, 256]', '[512, 512]']
  - `agent_network.critic_hidden_layer_sizes`: 3 levels — ['[256, 256, 256]', '[256, 256]', '[512, 512]']

**Fixed HPs:**
  - `discount` = 0.99
  - `gae_lambda` = 0.95
  - `gae_horizon` = 0
  - `clip_epsilon` = 0.2
  - `loss_weights.actor_loss` = 1
  - `loss_weights.critic_loss` = 0.5
  - `loss_weights.actor_entropy` = -0.01
  - `optimizer.grad_clip_norm` = 1.0
  - `normalize_obs` = True
  - `normalize_gae` = True
  - `minibatch_size` = 128
  - `reuse_rollout_epochs` = 4