优化 jepa.py 中通用 rollout 热路径：批量预编码动
作、移除循环内 torch.cat，并为 history_size==1 与环形缓冲区更新 添加更轻量实现； 收益不大
This commit is contained in:
qihuanye
@@ -1282,3 +1282,489 @@ evaluation_time: 44.974061727523804 seconds
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inference_precision: fp16
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inference_compile_target: predictor
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inference_compile_mode: reduce-overhead
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==== CONFIG ====
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cache_dir: null
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solver:
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_target_: stable_worldmodel.solver.CEMSolver
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model: ???
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batch_size: 1
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num_samples: 300
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var_scale: 1.0
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n_steps: 30
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topk: 30
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device: cuda
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seed: ${seed}
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world:
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env_name: swm/TwoRoom-v1
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num_envs: ${eval.num_eval}
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max_episode_steps: 100
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history_size: 1
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frame_skip: 1
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seed: 42
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policy: two-room/tworoom/lejepa
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inference_precision: fp16
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dataset:
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stats: ${eval.dataset_name}
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keys_to_cache:
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- action
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- proprio
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plan_config:
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horizon: 5
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receding_horizon: 5
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action_block: 5
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eval:
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num_eval: 50
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goal_offset_steps: 25
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eval_budget: 50
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img_size: 224
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dataset_name: tworoom
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callables:
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- method: _set_state
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args:
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state:
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value: proprio
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- method: _set_goal_state
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args:
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goal_state:
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value: goal_proprio
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output:
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filename: tworoom_results.txt
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==== RESULTS ====
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metrics: {'success_rate': 88.0, 'episode_successes': array([ True, False, True, False, True, True, True, True, False,
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True, True, True, True, True, True, True, True, True,
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True, True, True, True, True, True, True, True, True,
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True, True, True, True, False, True, True, True, True,
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True, True, False, False, True, True, True, True, True,
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True, True, True, True, True]), 'seeds': None}
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evaluation_time: 102.31317353248596 seconds
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inference_precision: fp16
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inference_compile_target: predictor
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inference_compile_mode: reduce-overhead
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==== CONFIG ====
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cache_dir: null
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solver:
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_target_: stable_worldmodel.solver.CEMSolver
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model: ???
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batch_size: 1
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num_samples: 300
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var_scale: 1.0
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n_steps: 30
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topk: 30
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device: cuda
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seed: ${seed}
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world:
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env_name: swm/TwoRoom-v1
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num_envs: ${eval.num_eval}
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max_episode_steps: 100
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history_size: 1
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frame_skip: 1
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seed: 42
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policy: two-room/tworoom/lejepa
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inference_precision: fp16
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dataset:
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stats: ${eval.dataset_name}
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keys_to_cache:
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- action
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- proprio
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plan_config:
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horizon: 5
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receding_horizon: 5
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action_block: 5
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eval:
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num_eval: 50
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goal_offset_steps: 25
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eval_budget: 50
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img_size: 224
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dataset_name: tworoom
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callables:
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- method: _set_state
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args:
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state:
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value: proprio
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- method: _set_goal_state
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args:
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goal_state:
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value: goal_proprio
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output:
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filename: tworoom_results.txt
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==== RESULTS ====
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metrics: {'success_rate': 88.0, 'episode_successes': array([ True, False, True, False, True, True, True, True, False,
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True, True, True, True, True, True, True, True, True,
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True, True, True, True, True, True, True, True, True,
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True, True, True, True, False, True, True, True, True,
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True, True, False, False, True, True, True, True, True,
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True, True, True, True, True]), 'seeds': None}
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evaluation_time: 45.355348110198975 seconds
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inference_precision: fp16
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inference_compile_target: predictor
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inference_compile_mode: reduce-overhead
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==== CONFIG ====
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cache_dir: null
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solver:
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_target_: stable_worldmodel.solver.CEMSolver
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model: ???
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batch_size: 1
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num_samples: 300
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var_scale: 1.0
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n_steps: 30
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topk: 30
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device: cuda
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seed: ${seed}
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world:
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env_name: swm/TwoRoom-v1
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num_envs: ${eval.num_eval}
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max_episode_steps: 100
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history_size: 1
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frame_skip: 1
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seed: 42
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policy: two-room/tworoom/lejepa
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inference_precision: fp16
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dataset:
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stats: ${eval.dataset_name}
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keys_to_cache:
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- action
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- proprio
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plan_config:
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horizon: 5
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receding_horizon: 5
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action_block: 5
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eval:
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num_eval: 50
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goal_offset_steps: 25
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eval_budget: 50
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img_size: 224
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dataset_name: tworoom
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callables:
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- method: _set_state
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args:
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state:
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value: proprio
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- method: _set_goal_state
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args:
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goal_state:
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value: goal_proprio
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output:
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filename: tworoom_results.txt
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profile:
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enabled: true
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export_tensorboard: false
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export_chrome_trace: false
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==== RESULTS ====
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metrics: {'success_rate': 88.0, 'episode_successes': array([ True, False, True, False, True, True, True, True, False,
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True, True, True, True, True, True, True, True, True,
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True, True, True, True, True, True, True, True, True,
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True, True, True, True, False, True, True, True, True,
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True, True, False, False, True, True, True, True, True,
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True, True, True, True, True]), 'seeds': None}
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evaluation_time: 110.91939687728882 seconds
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inference_precision: fp16
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inference_compile_target: predictor
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inference_compile_mode: reduce-overhead
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profile_dir: /mnt/ASC1637/lewm_baseline/le-wm/torch_profile
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profile_summary: /mnt/ASC1637/lewm_baseline/le-wm/torch_profile/key_averages.txt
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==== CONFIG ====
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cache_dir: null
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solver:
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_target_: stable_worldmodel.solver.CEMSolver
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model: ???
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batch_size: 1
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num_samples: 300
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var_scale: 1.0
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n_steps: 30
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topk: 30
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device: cuda
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seed: ${seed}
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world:
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env_name: swm/TwoRoom-v1
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num_envs: ${eval.num_eval}
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max_episode_steps: 100
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history_size: 1
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frame_skip: 1
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seed: 42
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policy: two-room/tworoom/lejepa
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inference_precision: fp16
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dataset:
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stats: ${eval.dataset_name}
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keys_to_cache:
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- action
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- proprio
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plan_config:
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horizon: 5
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receding_horizon: 5
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action_block: 5
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eval:
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num_eval: 50
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goal_offset_steps: 25
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eval_budget: 50
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img_size: 224
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dataset_name: tworoom
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callables:
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- method: _set_state
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args:
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state:
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value: proprio
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- method: _set_goal_state
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args:
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goal_state:
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value: goal_proprio
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output:
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filename: tworoom_results.txt
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==== RESULTS ====
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metrics: {'success_rate': 88.0, 'episode_successes': array([ True, False, True, False, True, True, True, True, False,
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True, True, True, True, True, True, True, True, True,
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True, True, True, False, True, True, True, True, True,
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True, True, True, True, False, True, True, True, True,
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True, True, False, True, True, True, True, True, True,
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True, True, True, True, True]), 'seeds': None}
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evaluation_time: 54.21496343612671 seconds
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inference_precision: fp16
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inference_compile_target: predictor
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inference_compile_mode: reduce-overhead
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==== CONFIG ====
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cache_dir: null
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solver:
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_target_: stable_worldmodel.solver.CEMSolver
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model: ???
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batch_size: 1
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num_samples: 300
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var_scale: 1.0
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n_steps: 30
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topk: 30
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device: cuda
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seed: ${seed}
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world:
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env_name: swm/TwoRoom-v1
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num_envs: ${eval.num_eval}
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max_episode_steps: 100
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history_size: 1
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frame_skip: 1
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seed: 42
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policy: two-room/tworoom/lejepa
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inference_precision: fp16
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dataset:
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stats: ${eval.dataset_name}
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keys_to_cache:
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- action
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- proprio
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plan_config:
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horizon: 5
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receding_horizon: 5
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action_block: 5
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eval:
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num_eval: 50
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goal_offset_steps: 25
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eval_budget: 50
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img_size: 224
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dataset_name: tworoom
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callables:
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- method: _set_state
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args:
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state:
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value: proprio
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- method: _set_goal_state
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args:
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goal_state:
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value: goal_proprio
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output:
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filename: tworoom_results.txt
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==== RESULTS ====
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metrics: {'success_rate': 90.0, 'episode_successes': array([ True, False, True, False, True, True, True, True, False,
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True, True, True, True, True, True, True, True, True,
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True, True, True, False, True, True, True, True, True,
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True, True, True, True, False, True, True, True, True,
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True, True, True, True, True, True, True, True, True,
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True, True, True, True, True]), 'seeds': None}
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evaluation_time: 43.69562244415283 seconds
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inference_precision: fp16
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inference_compile_target: predictor
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inference_compile_mode: reduce-overhead
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==== CONFIG ====
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cache_dir: null
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solver:
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_target_: stable_worldmodel.solver.CEMSolver
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model: ???
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batch_size: 1
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num_samples: 300
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var_scale: 1.0
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n_steps: 30
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topk: 30
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device: cuda
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seed: ${seed}
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world:
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env_name: swm/TwoRoom-v1
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num_envs: ${eval.num_eval}
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max_episode_steps: 100
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history_size: 1
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frame_skip: 1
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seed: 42
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policy: two-room/tworoom/lejepa
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inference_precision: fp16
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dataset:
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stats: ${eval.dataset_name}
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keys_to_cache:
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- action
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- proprio
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plan_config:
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horizon: 5
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receding_horizon: 5
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action_block: 5
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eval:
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num_eval: 50
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goal_offset_steps: 25
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eval_budget: 50
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img_size: 224
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dataset_name: tworoom
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callables:
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- method: _set_state
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args:
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state:
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value: proprio
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- method: _set_goal_state
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args:
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goal_state:
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value: goal_proprio
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output:
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filename: tworoom_results.txt
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==== RESULTS ====
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metrics: {'success_rate': 88.0, 'episode_successes': array([ True, False, True, False, True, True, True, True, False,
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True, True, True, True, True, True, True, True, True,
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True, True, True, False, True, True, True, True, True,
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True, True, True, True, False, True, True, True, True,
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True, True, False, True, True, True, True, True, True,
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True, True, True, True, True]), 'seeds': None}
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evaluation_time: 42.99847435951233 seconds
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inference_precision: fp16
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inference_compile_target: predictor
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inference_compile_mode: reduce-overhead
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==== CONFIG ====
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cache_dir: null
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solver:
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_target_: stable_worldmodel.solver.CEMSolver
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model: ???
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batch_size: 1
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num_samples: 300
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var_scale: 1.0
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n_steps: 30
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topk: 30
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device: cuda
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seed: ${seed}
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world:
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env_name: swm/TwoRoom-v1
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num_envs: ${eval.num_eval}
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max_episode_steps: 100
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history_size: 1
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frame_skip: 1
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seed: 42
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policy: two-room/tworoom/lejepa
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inference_precision: fp16
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dataset:
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stats: ${eval.dataset_name}
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keys_to_cache:
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||||
- action
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- proprio
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plan_config:
|
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horizon: 5
|
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receding_horizon: 5
|
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action_block: 5
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eval:
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num_eval: 50
|
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goal_offset_steps: 25
|
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eval_budget: 50
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img_size: 224
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dataset_name: tworoom
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callables:
|
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- method: _set_state
|
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args:
|
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state:
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value: proprio
|
||||
- method: _set_goal_state
|
||||
args:
|
||||
goal_state:
|
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value: goal_proprio
|
||||
output:
|
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filename: tworoom_results.txt
|
||||
|
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==== RESULTS ====
|
||||
metrics: {'success_rate': 88.0, 'episode_successes': array([ True, False, True, False, True, True, True, True, False,
|
||||
True, True, True, True, True, True, True, True, True,
|
||||
True, True, True, False, True, True, True, True, True,
|
||||
True, True, True, True, False, True, True, True, True,
|
||||
True, True, False, True, True, True, True, True, True,
|
||||
True, True, True, True, True]), 'seeds': None}
|
||||
evaluation_time: 43.14276576042175 seconds
|
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inference_precision: fp16
|
||||
inference_compile_target: predictor
|
||||
inference_compile_mode: reduce-overhead
|
||||
|
||||
==== CONFIG ====
|
||||
cache_dir: null
|
||||
solver:
|
||||
_target_: stable_worldmodel.solver.CEMSolver
|
||||
model: ???
|
||||
batch_size: 1
|
||||
num_samples: 300
|
||||
var_scale: 1.0
|
||||
n_steps: 30
|
||||
topk: 30
|
||||
device: cuda
|
||||
seed: ${seed}
|
||||
world:
|
||||
env_name: swm/TwoRoom-v1
|
||||
num_envs: ${eval.num_eval}
|
||||
max_episode_steps: 100
|
||||
history_size: 1
|
||||
frame_skip: 1
|
||||
seed: 42
|
||||
policy: two-room/tworoom/lejepa
|
||||
inference_precision: fp16
|
||||
dataset:
|
||||
stats: ${eval.dataset_name}
|
||||
keys_to_cache:
|
||||
- action
|
||||
- proprio
|
||||
plan_config:
|
||||
horizon: 5
|
||||
receding_horizon: 5
|
||||
action_block: 5
|
||||
eval:
|
||||
num_eval: 50
|
||||
goal_offset_steps: 25
|
||||
eval_budget: 50
|
||||
img_size: 224
|
||||
dataset_name: tworoom
|
||||
callables:
|
||||
- method: _set_state
|
||||
args:
|
||||
state:
|
||||
value: proprio
|
||||
- method: _set_goal_state
|
||||
args:
|
||||
goal_state:
|
||||
value: goal_proprio
|
||||
output:
|
||||
filename: tworoom_results.txt
|
||||
|
||||
==== RESULTS ====
|
||||
metrics: {'success_rate': 88.0, 'episode_successes': array([ True, False, True, False, True, True, True, True, False,
|
||||
True, True, True, True, True, True, True, True, True,
|
||||
True, True, True, False, True, True, True, True, True,
|
||||
True, True, True, True, False, True, True, True, True,
|
||||
True, True, False, True, True, True, True, True, True,
|
||||
True, True, True, True, True]), 'seeds': None}
|
||||
evaluation_time: 43.71034002304077 seconds
|
||||
inference_precision: fp16
|
||||
inference_compile_target: predictor
|
||||
inference_compile_mode: reduce-overhead
|
||||
|
||||
Reference in New Issue
Block a user