35 lines
1.4 KiB
Python
35 lines
1.4 KiB
Python
import rltorch
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config = {}
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config['seed'] = 901
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config['zoom'] = 4
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config['environment_name'] = 'PongNoFrameskip-v4'
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config['learning_rate'] = 1e-4
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config['target_sync_tau'] = 1e-3
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config['discount_rate'] = 0.99
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config['exploration_rate'] = rltorch.scheduler.ExponentialScheduler(initial_value = 1, end_value = 0.02, iterations = 10**5)
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config['replay_skip'] = 4
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config['batch_size'] = 32 * (config['replay_skip'] + 1)
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config['num_sneaky_episodes'] = 10 # per loop
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config['disable_cuda'] = False
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config['seconds_play_per_state'] = 120
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config['seconds_play_per_state'] = 5
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# 30 transitions per second for 120 seconds = 3600 transitions per turn
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config['memory_size'] = 86400
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config['dqfd_demo_loss_weight'] = 0.01
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config['dqfd_td_loss_weight'] = 1.
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config['demo_prio_bonus'] = 0.
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config['observed_prio_bonus'] = 0.
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# Prioritized vs Random Sampling
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# 0 - Random sampling
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# 1 - Only the highest prioirities
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config['prioritized_replay_sampling_priority'] = 0.6
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config['prioritized_replay_sampling_priority'] = 0.
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# How important are the weights for the loss?
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# 0 - Treat all losses equally
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# 1 - Lower the importance of high losses
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# Should ideally start from 0 and move your way to 1 to prevent overfitting
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config['prioritized_replay_weight_importance'] = rltorch.scheduler.ExponentialScheduler(initial_value = 0.4, end_value = 1, iterations = 10**5)
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config['prioritized_replay_weight_importance'] = 0.
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