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basketball_code
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### ClothFold
python train_PEBBLE.py \
    env=softgym_ClothFoldDiagonal \
    seed=0 \
    vlm_label=1 \
    vlm=gpt4v_two_image \
    exp_name=test \
    reward=learn_from_preference \
    image_reward=1 \
    num_train_steps=15000 \
    agent.params.actor_lr=0.0005 agent.params.critic_lr=0.0005 gradient_update=1 activation=tanh num_unsup_steps=250 \
    num_interact=1000 max_feedback=500 \
    reward_batch=50 reward_update=25 \
    resnet=1 \
    feed_type=0 teacher_beta=-1 teacher_gamma=1 teacher_eps_mistake=0 \
    teacher_eps_skip=0 teacher_eps_equal=0 segment=1 num_seed_steps=250 \
    eval_frequency=250 num_eval_episodes=1 \
    cached_label_path=data/cached_labels/ClothFold/seed_0/


### soccer
python train_PEBBLE.py \
    env=metaworld_soccer-v2 \
    seed=0 \
    exp_name=reproduce \
    reward=learn_from_preference \
    vlm_label=1 \
    vlm=gemini_free_form \
    image_reward=1 \
    reward_batch=40 \
    segment=1 \
    teacher_eps_mistake=0 \
    reward_update=5 \
    num_interact=4000 \
    max_feedback=20000 \
    reward_lr=1e-4 \
    agent.params.actor_lr=0.0003 agent.params.critic_lr=0.0003 gradient_update=1 activation=tanh num_unsup_steps=9000 \
    num_train_steps=1000000 agent.params.batch_size=512 double_q_critic.params.hidden_dim=256 double_q_critic.params.hidden_depth=3 \
    diag_gaussian_actor.params.hidden_dim=256 diag_gaussian_actor.params.hidden_depth=3  \
    feed_type=0 teacher_beta=-1 teacher_gamma=1  teacher_eps_skip=0 teacher_eps_equal=0 \
    num_eval_episodes=1 \
    cached_label_path=data/cached_labels/Soccer/seed_1/


### drawer
python train_PEBBLE.py \
    env=metaworld_drawer-open-v2 \
    seed=0 \
    exp_name=reproduce \
    reward=learn_from_preference \
    vlm_label=1 \
    vlm=gemini_free_form \
    image_reward=1 \
    reward_batch=40 \
    segment=1 \
    teacher_eps_mistake=0 \
    reward_update=10 \
    num_interact=4000 \
    max_feedback=20000 \
    agent.params.actor_lr=0.0003 agent.params.critic_lr=0.0003 gradient_update=1 activation=tanh num_unsup_steps=9000 \
    num_train_steps=1000000 agent.params.batch_size=512 double_q_critic.params.hidden_dim=256 double_q_critic.params.hidden_depth=3 \
    diag_gaussian_actor.params.hidden_dim=256 diag_gaussian_actor.params.hidden_depth=3  \
    feed_type=0 teacher_beta=-1 teacher_gamma=1  teacher_eps_skip=0 teacher_eps_equal=0 \
    num_eval_episodes=1 \
    cached_label_path=data/cached_labels/Drawer/seed_0/


### sweep into
python train_PEBBLE.py \
    env=metaworld_sweep-into-v2 \
    seed=0 \
    exp_name=reproduce \
    reward=learn_from_preference \
    vlm_label=1 \
    vlm=gemini_free_form \
    image_reward=1 \
    reward_batch=40 \
    segment=1 \
    teacher_eps_mistake=0 \
    reward_update=10 \
    num_interact=4000 \
    max_feedback=20000 \
    agent.params.actor_lr=0.0003 agent.params.critic_lr=0.0003 gradient_update=1 activation=tanh num_unsup_steps=9000 \
    num_train_steps=1000000 agent.params.batch_size=512 double_q_critic.params.hidden_dim=256 double_q_critic.params.hidden_depth=3 \
    diag_gaussian_actor.params.hidden_dim=256 diag_gaussian_actor.params.hidden_depth=3  \
    feed_type=0 teacher_beta=-1 teacher_gamma=1  teacher_eps_skip=0 teacher_eps_equal=0 \
    num_eval_episodes=1 \
    cached_label_path=data/cached_labels/Sweep-Into/combined/

### cartpole
python train_PEBBLE.py \
    env=CartPole-v1 \
    seed=0 \
    exp_name=reproduce \
    reward=learn_from_preference \
    vlm=gemini_free_form \
    vlm_label=1 \
    exp_name=2024-3-24-icml-rebuttal-more-seeds \
    segment=1 \
    image_reward=1 \
    max_feedback=10000 reward_batch=50 reward_update=50 \
    num_interact=5000 \
    num_train_steps=500000 \
    agent.params.critic_lr=0.0005 gradient_update=1 activation=tanh num_unsup_steps=1000 num_train_steps=500000   \
    feed_type=0 teacher_beta=-1 teacher_gamma=1 teacher_eps_mistake=0 teacher_eps_skip=0 teacher_eps_equal=0  \
    agent.params.actor_lr=0.0005 \
    cached_label_path=data/cached_labels/CartPole/seed_0/

# RopeFlattenEasy
python train_PEBBLE.py \
    env=softgym_RopeFlattenEasy \
    seed=0 \
    exp_name=reproduce \
    reward=learn_from_preference \
    vlm_label=1 \
    vlm=gemini_free_form \
    image_reward=1 \
    reward_batch=100 \
    segment=1 \
    teacher_eps_mistake=0 \
    reward_update=30 \
    num_interact=5000 \
    max_feedback=20000 \
    reward_lr=1e-4 \
    agent.params.actor_lr=0.0003 agent.params.critic_lr=0.0003 gradient_update=1 activation=tanh num_unsup_steps=9000 \
    num_train_steps=600000 agent.params.batch_size=512 double_q_critic.params.hidden_dim=256 double_q_critic.params.hidden_depth=3 \
    diag_gaussian_actor.params.hidden_dim=256 diag_gaussian_actor.params.hidden_depth=3  \
    feed_type=0 teacher_beta=-1 teacher_gamma=1  teacher_eps_skip=0 teacher_eps_equal=0 \
    num_eval_episodes=1 \
    resnet=1 \
    cached_label_path=data/cached_labels/RopeFlattenEasy/seed_0/

# PassWater
python train_PEBBLE.py \
    env=softgym_PassWater \
    seed=0 \
    exp_name=reproduce \
    reward=learn_from_preference \
    vlm_label=1 \
    vlm=gemini_free_form \
    image_reward=1 \
    reward_batch=100 \
    segment=1 \
    teacher_eps_mistake=0 \
    reward_update=30 \
    num_interact=5000 \
    max_feedback=20000 \
    reward_lr=1e-4 \
    agent.params.actor_lr=0.0003 agent.params.critic_lr=0.0003 gradient_update=1 activation=tanh num_unsup_steps=9000 \
    num_train_steps=600000 agent.params.batch_size=512 double_q_critic.params.hidden_dim=256 double_q_critic.params.hidden_depth=3 \
    diag_gaussian_actor.params.hidden_dim=256 diag_gaussian_actor.params.hidden_depth=3  \
    feed_type=0 teacher_beta=-1 teacher_gamma=1  teacher_eps_skip=0 teacher_eps_equal=0 \
    num_eval_episodes=1 \
    resnet=1 \
    cached_label_path=data/cached_labels/PassWater/seed_0/