diff --git a/environments/logging/monitor.py b/environments/logging/monitor.py index 9ded10b..b4b85fe 100644 --- a/environments/logging/monitor.py +++ b/environments/logging/monitor.py @@ -74,13 +74,13 @@ class MonitorCallback(BaseCallback): dones = alt_dones elif self.locals.get('dones', None) is not None: dones =self.locals.get('dones', None) - elif self.locals.get('dones', None) is not None: + elif self.locals.get('done', None) is not None: dones = self.locals.get('done', [None]) else: dones = [] for env_idx, (info, done) in enumerate(zip(infos, dones)): - self._monitor_dicts[env_idx][self.num_timesteps - env_idx] = {key: val for key, val in info.items() + self._monitor_dicts[env_idx][len(self._monitor_dicts[env_idx])] = {key: val for key, val in info.items() if key not in ['terminal_observation', 'episode'] and not key.startswith('rec_')} if done: diff --git a/environments/logging/plotting.py b/environments/logging/plotting.py index 1fa5b15..b93bab1 100644 --- a/environments/logging/plotting.py +++ b/environments/logging/plotting.py @@ -34,7 +34,7 @@ def prepare_plot(filepath, results_df, ext='png', hue='Measurement', style=None) sns.set(rc={'text.usetex': True}, style='whitegrid') lineplot = sns.lineplot(data=df, x='Episode', y='Score', ci=95, palette=PALETTE, hue_order=hue_order, hue=hue, style=style) - lineplot.set_title(f'{sorted(list(df["Measurement"].unique()))}') + # lineplot.set_title(f'{sorted(list(df["Measurement"].unique()))}') plot(filepath, ext=ext) # plot raises errors not lineplot! except (FileNotFoundError, RuntimeError): print('Struggling to plot Figure using LaTeX - going back to normal.') @@ -42,5 +42,5 @@ def prepare_plot(filepath, results_df, ext='png', hue='Measurement', style=None) sns.set(rc={'text.usetex': False}, style='whitegrid') lineplot = sns.lineplot(data=df, x='Episode', y='Score', hue=hue, style=style, ci=95, palette=PALETTE, hue_order=hue_order) - lineplot.set_title(f'{sorted(list(df["Measurement"].unique()))}') + # lineplot.set_title(f'{sorted(list(df["Measurement"].unique()))}') plot(filepath, ext=ext) diff --git a/main.py b/main.py index d64a472..06c9958 100644 --- a/main.py +++ b/main.py @@ -34,18 +34,20 @@ def combine_runs(run_path: Union[str, PathLike]): df_list.append(monitor_df) df = pd.concat(df_list, ignore_index=True) - df = df.fillna(0).rename(columns={'episode': 'Episode', 'run': 'Run'}) + df = df.fillna(0).rename(columns={'episode': 'Episode', 'run': 'Run'}).sort_values(['Run', 'Episode']) columns = [col for col in df.columns if col not in IGNORED_DF_COLUMNS] roll_n = 50 - skip_n = 40 non_overlapp_window = df.groupby(['Run', 'Episode']).rolling(roll_n, min_periods=1).mean() df_melted = non_overlapp_window[columns].reset_index().melt(id_vars=['Episode', 'Run'], value_vars=columns, var_name="Measurement", value_name="Score") - df_melted = df_melted[df_melted['Episode'] % skip_n == 0] + + if df_melted['Episode'].max() > 100: + skip_n = round(df_melted['Episode'].max() * 0.01) + df_melted = df_melted[df_melted['Episode'] % skip_n == 0] prepare_plot(run_path / f'{run_path.name}_monitor_lineplot.png', df_melted) print('Plotting done.') @@ -71,14 +73,15 @@ def compare_runs(run_path: Path, run_identifier: int, parameter: Union[str, List columns = [col for col in df.columns if col in parameter] roll_n = 40 - skip_n = 20 non_overlapp_window = df.groupby(['Model', 'Run', 'Episode']).rolling(roll_n, min_periods=1).mean() df_melted = non_overlapp_window[columns].reset_index().melt(id_vars=['Episode', 'Run', 'Model'], value_vars=columns, var_name="Measurement", value_name="Score") - df_melted = df_melted[df_melted['Episode'] % skip_n == 0] + if df_melted['Episode'].max() > 100: + skip_n = round(df_melted['Episode'].max() * 0.01) + df_melted = df_melted[df_melted['Episode'] % skip_n == 0] style = 'Measurement' if len(columns) > 1 else None prepare_plot(run_path / f'{run_identifier}_compare_{parameter}.png', df_melted, hue='Model', style=style) @@ -113,7 +116,7 @@ if __name__ == '__main__': move_props = MovementProperties(allow_diagonal_movement=False, allow_square_movement=True, allow_no_op=False) - train_steps = 1e6 + train_steps = 1e5 time_stamp = int(time.time()) out_path = None @@ -131,12 +134,11 @@ if __name__ == '__main__': cast_shadows=True, doors_have_area=False, env_seed=seed, verbose=False, ) - # env = make_env(env_kwargs)() - env = SubprocVecEnv([make_env(env_kwargs) for _ in range(12)], start_method="spawn") - if modeL_type.__name__ in ["PPO", "A2C"]: kwargs = dict(ent_coef=0.01) + env = SubprocVecEnv([make_env(env_kwargs) for _ in range(6)], start_method="spawn") elif modeL_type.__name__ in ["RegDQN", "DQN", "QRDQN"]: + env = make_env(env_kwargs)() kwargs = dict(buffer_size=50000, learning_starts=64, batch_size=64, @@ -145,6 +147,7 @@ if __name__ == '__main__': exploration_final_eps=0.025) else: raise NameError(f'The model "{modeL_type.__name__}" has the wrong name.') + model = modeL_type("MlpPolicy", env, verbose=1, seed=seed, device='cpu', **kwargs) out_path = Path('debug_out') / f'{model.__class__.__name__}_{time_stamp}' @@ -165,7 +168,11 @@ if __name__ == '__main__': save_path = out_path / f'model_{identifier}.zip' save_path.parent.mkdir(parents=True, exist_ok=True) model.save(save_path) - env.env_method('save_params', out_path.parent / f'env_{model.__class__.__name__}_{time_stamp}.yaml') + param_path = out_path.parent / f'env_{model.__class__.__name__}_{time_stamp}.yaml' + try: + env.env_method('save_params', param_path) + except AttributeError: + env.save_params(param_path) print("Model Trained and saved") print("Model Group Done.. Plotting...")