@@ -26,7 +26,6 @@ def __init__(self,
2626 algorithm_ctor = None ,
2727 data_transformer_ctor = None ,
2828 random_seed = None ,
29- skip_torch_deterministic = False ,
3029 num_iterations = 1000 ,
3130 num_env_steps = 0 ,
3231 unroll_length = 8 ,
@@ -99,8 +98,6 @@ def __init__(self,
9998 will not be normalized. Data will be in mismatch, causing training to
10099 suffer and potentially fail.
101100 random_seed (None|int): random seed, a random seed is used if None
102- skip_torch_deterministic (bool): if True, turns of
103- ``torch.use_deterministic_algorithms`` even when a random_seed is set.
104101 num_iterations (int): For RL trainer, indicates number of update
105102 iterations (ignored if 0). Note that for off-policy algorithms, if
106103 ``initial_collect_steps>0``, then the first
@@ -271,7 +268,6 @@ def __init__(self,
271268 self .data_transformer_ctor = data_transformer_ctor
272269 self .data_transformer = None # to be set by Trainer
273270 self .random_seed = random_seed
274- self .skip_torch_deterministic = skip_torch_deterministic
275271 self .num_iterations = num_iterations
276272 self .num_env_steps = num_env_steps
277273 self .unroll_length = unroll_length
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