Datasets:
Tasks:
Tabular Regression
Size:
10K - 100K
Tags:
reinforcement-learning
PPO
hyperparameter-optimization
fitness-landscape-analysis
parallel-rl
automl
License:
File size: 3,896 Bytes
9c82ccd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 | # 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
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