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Model-Based RL: Player (tensorflow#1330)
* SimulatedEnv with gym-like interface. * Initial Player * Player: Add reward header, keybord reset, CLI, move to player.py * Player: add WAIT mode, few CLI options. * Introduce Policy Inferencer * Recording videos for ppo and player, some refactoring. * Player refactor. Add real env recording with PPO agent. * Extend CLI documentation. Remove some imports. * Pylint * Extend documentation. * Correct dopamine import. * Move gym.utils.play to global imports. * Remove SimulatedEnv (unnecesarry wrapper for FlatBatchEnv<SimulatedBatchGymEnv>) * Replace join_and_check with os.path.join. * Move generation of initial_frame_chooser function to rl_utils. * Move make_simulated_env_fn from trainer_model_based.py to rl.py * Remove trainer_model_based imports, clean up player and record_ppo FLAGS. * Move setup_env and load_t2t_gym_env to T2TGymEnv. * Correct relative imports. * Custom policy world_model and data paths for player. * Enable BatchGymEnv to load directly from checkpoint file. * Small fix record_ppo. * Remove unused record_ppo.py.
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