Update data instances and data fields

README.md

@@ -536,11 +536,10 @@ The PPO RL agent was trained for 25M steps on each environment and obtained the
 
 ## Dataset Structure
 ### Data Instances
-Each data instance represents a single step consisting of tuples of the form (observation, action, reward, done, truncated) = (o_t, a_t, r_{t+1}, done_{t+1}, trunc_{t+1}).
+Each data instance represents a single step consisting of tuples of the form (observation, action, reward, done, truncated) = (o_t, a_t, r_t, terminated_t, truncated_t).
 
 ```json
 {'action': 1,
- 'done': False,
  'observation': [[[0, 166, 253],
                   [0, 174, 255],
                   [0, 170, 251],
@@ -568,15 +567,16 @@ Each data instance represents a single step consisting of tuples of the form (ob
                   [0, 0, 37],
                   [0, 0, 39]]],
  'reward': 0.0,
+ 'terminated': False,
  'truncated': False}
 ```
 
 ### Data Fields
 - `observation`: The current RGB observation from the environment.
 - `action`: The action predicted by the agent for the current observation.
-- `reward`: The received reward from stepping the environment with the current action.
-- `done`: If the new observation is the start of a new episode. Obtained after stepping the environment with the current action.
-- `truncated`: If the new observation is the start of a new episode due to truncation. Obtained after stepping the environment with the current action.
+- `reward`: The received reward for the current observation.
+- `terminated`: If the episode has terminated with the current observation.
+- `truncated`: If the episode is truncated with the current observation.
 
 ### Data Splits
 The dataset is divided into a `train` (90%) and `test` (10%) split. Each environment-dataset has in sum 10M steps (data points).