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- general:
- experiment_tag: 'lstm_drqn_'
- env_id: 'twcc_easy_level15_gamesize100_step50_seed9_train'
- run_test: True
- valid_env_id: 'twcc_easy_level15_gamesize10_step50_seed9_validation'
- test_env_id: []
- # test_env_id: ['twcc_easy_level10_gamesize10_step50_seed0_test']
- discount_gamma: 0.5
- random_seed: 42
- observation_cache_capacity: 1 # concat window of history observation, 1 means no history observations available
- experiments_dir: 'experiments/summary_env_easy_100'
- use_cuda: True # disable this when running on machine without cuda
- provide_prev_action: True
- # replay memory
- history_size: 8
- update_from: 4
- replay_memory_capacity: 500000
- replay_memory_priority_fraction: 0.25 # 0.0 to disable this
- update_per_k_game_steps: 4
- replay_batch_size: 32
- # epsilon greedy
- epsilon_anneal_epochs: 2000 # -1 if not annealing
- epsilon_anneal_from: 1.0
- epsilon_anneal_to: 0.2
- # recombinant episodes
- recombine_eps_from: 500 # -1 if not annealing
- recombine_anneal_from: 0.1
- recombine_anneal_to: 0.8
- # counting reward
- revisit_counting: True
- revisit_counting_lambda_anneal_from: 1.0
- revisit_counting_lambda_anneal_epochs: -1 # -1 if not annealing
- revisit_counting_lambda_anneal_to: 0.0
- training:
- scheduling:
- batch_size: 10
- test_batch_size: 10
- epoch: 6000
- model_checkpoint_path: 'saved_models/summary_model_dqrn_easy_100.pt'
- logging_frequency: 20
- optimizer:
- step_rule: 'adam' # adam, sgd
- learning_rate: 0.001
- clip_grad_norm: 5
- model:
- lstm_dqn:
- embedding_size: 20
- encoder_rnn_hidden_size: [100]
- action_scorer_hidden_dim: 64
- dropout_between_rnn_layers: 0.
- bootstrap:
- filter_sentence: False
- threshold: 0.3
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