
### Running training
- Num examples = 1000
- Num Epochs = 120
- Instantaneous batch size per device = 64
- Total train batch size = 64
- Gradient Accumulation steps = 1
- Total optimization steps = 1920
  
### Train Output
- global_step = 1920
- train_runtime = 1849.2158
- train_samples_per_second = 64.892
- train_steps_per_second = 1.038
- train_loss = 0.04717305501302083
- epoch = 120.0

### Dataset
- edbeeching/decision_transformer_gym_replay
  - halfcheetah-expert-v2
{"license": ["mit"], "tags": ["decisionTransformer", "deep reinforcement"], "datasets": ["edbeeching/decision_transformer_gym_replay"]}
transformers pytorch decision_transformer feature-extraction decisionTransformer deep reinforcement dataset:edbeeching/decision_transformer_gym_replay license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #decision_transformer #feature-extraction #decisionTransformer #deep reinforcement #dataset-edbeeching/decision_transformer_gym_replay #license-mit #endpoints_compatible #region-us 


### Running training
- Num examples = 1000
- Num Epochs = 120
- Instantaneous batch size per device = 64
- Total train batch size = 64
- Gradient Accumulation steps = 1
- Total optimization steps = 1920
  
### Train Output
- global_step = 1920
- train_runtime = 1849.2158
- train_samples_per_second = 64.892
- train_steps_per_second = 1.038
- train_loss = 0.04717305501302083
- epoch = 120.0

### Dataset
- edbeeching/decision_transformer_gym_replay
  - halfcheetah-expert-v2
### Running training
- Num examples = 1000
- Num Epochs = 120
- Instantaneous batch size per device = 64
- Total train batch size = 64
- Gradient Accumulation steps = 1
- Total optimization steps = 1920 ### Train Output
- global_step = 1920
- train_runtime = 1849.2158
- train_samples_per_second = 64.892
- train_steps_per_second = 1.038
- train_loss = 0.04717305501302083
- epoch = 120.0 ### Dataset
- edbeeching/decision_transformer_gym_replay
  - halfcheetah-expert-v2
TAGS
#transformers #pytorch #decision_transformer #feature-extraction #decisionTransformer #deep reinforcement #dataset-edbeeching/decision_transformer_gym_replay #license-mit #endpoints_compatible #region-us 
 ### Running training
- Num examples = 1000
- Num Epochs = 120
- Instantaneous batch size per device = 64
- Total train batch size = 64
- Gradient Accumulation steps = 1
- Total optimization steps = 1920 ### Train Output
- global_step = 1920
- train_runtime = 1849.2158
- train_samples_per_second = 64.892
- train_steps_per_second = 1.038
- train_loss = 0.04717305501302083
- epoch = 120.0 ### Dataset
- edbeeching/decision_transformer_gym_replay
  - halfcheetah-expert-v2
passage: TAGS
#transformers #pytorch #decision_transformer #feature-extraction #decisionTransformer #deep reinforcement #dataset-edbeeching/decision_transformer_gym_replay #license-mit #endpoints_compatible #region-us 
### Running training
- Num examples = 1000
- Num Epochs = 120
- Instantaneous batch size per device = 64
- Total train batch size = 64
- Gradient Accumulation steps = 1
- Total optimization steps = 1920### Train Output
- global_step = 1920
- train_runtime = 1849.2158
- train_samples_per_second = 64.892
- train_steps_per_second = 1.038
- train_loss = 0.04717305501302083
- epoch = 120.0### Dataset
- edbeeching/decision_transformer_gym_replay
  - halfcheetah-expert-v2