Resume mode #455 adds train@ resume mode and refactors the enjoy mode. See PR for detailed info. train@ usage example Specify train mode as train@{predir}, where {predir} is the data directory of the last training run, or simply uselatest` to use the latest. e.g.: python run_lab.py slm_lab/spec/benchmark/reinforce/reinforce_cartpole.json reinforce_cartpole train # terminate run before its completion # optionally edit the spec file in a past-future-consistent manner # run resume with either of the commands: python run_lab.py slm_lab/spec/benchmark/reinforce/reinforce_cartpole.json reinforce_cartpole train@latest # or to use a specific run folder python run_lab.py slm_lab/spec/benchmark/reinforce/reinforce_cartpole.json reinforce_cartpole train@data/reinforce_cartpole_2020_04_13_232521 enjoy mode refactor The train@ resume mode API allows for the enjoy mode to be refactored. Both share similar syntax. Continuing with the example above, to enjoy a train model, we now use: python run_lab.py slm_lab/spec/benchmark/reinforce/reinforce_cartpole.json reinforce_cartpole enjoy@data/reinforce_cartpole_2020_04_13_232521/reinforce_cartpole_t0_s0_spec.json Plotly and PyTorch update #453 updates Plotly to 4.5.4 and PyTorch to 1.3.1. #454 explicitly shuts down Plotly orca server after plotting to prevent zombie processes PPO batch size optimization #453 adds chunking to allow PPO to run on larger batch size by breaking up the forward loop. New OnPolicyCrossEntropy memory #446 adds a new OnPolicyCrossEntropy memory class. See PR for details. Credits to @ingambe.